ABSTRACT Negative transfer continues to limit the benefits of multi‐task learning (MTL) in harmful language detection, where related tasks must share representations without diluting task‐specific nuances. We introduce task awareness (TA), a methodological framework that explicitly conditions MTL models on the task they must solve. TA is instantiated through two complementary mechanisms: Task‐aware input (TAI), which augments textual inputs with natural‐language task descriptions, and task embedding (TE), which learns task‐specific transformations guided by a task identification vector. Together they enable the encoder to disentangle shared and task‐dependent signals, reducing interference during joint optimisation. We integrate TA with BETO and AraBERT encoders and evaluate on six Spanish and Arabic datasets covering sexism, toxicity, offensive language, and hate speech. Across cross‐validation and official train‐test splits, TA consistently mitigates negative transfer, surpasses single‐task and conventional MTL baselines, and yields new state‐of‐the‐art scores on EXIST‐2021, HatEval‐2019, and HSArabic‐2023. The proposed methodology therefore combines a principled architectural innovation with demonstrated practical gains for multilingual harmful language detection. The resources to reproduce our experiments are publicly available at https://github.com/AngelFelipeMP/Arabic‐MultiTask‐Learning .

ABSTRACT Online dissemination of conspiracy theories (CTs) during epidemics poses significant risks to public health. This paper addresses the problem of detecting CTs in social media posts with an emphasis on the resource‐constrained scenarios characterized by the scarcity of labelled datasets and expert annotations, and the lack of computational budget for large‐scale LLM inference. To address these challenges, we investigate resource‐efficient methods for CT detection across multiple epidemics. We construct a novel dataset of CT‐labelled social media posts covering four major epidemics from the past decade: Ebola, Zika, COVID‐19 and Monkeypox. We conduct extensive experiments addressing four research questions: (1) the performance of BERT‐like models on individual epidemics, (2) the ability to transfer knowledge from past epidemics to new ones, (3) the efficacy of zero‐shot classification using Large Language Models (LLMs) and (4) the feasibility of training BERT‐like models on LLM‐labelled datasets. Our findings indicate that BERT‐like models exhibit highly variable performance across epidemics. Transfer learning from prior epidemics can be effective and their performance can be improved with the number of prior datasets. Zero‐shot LLM classifiers, including ensemble methods, achieve performance that matches or surpasses that of fine‐tuned BERT‐like models. Finally, we demonstrate that BERT‐like models trained on LLM‐labelled datasets achieve results close to the models trained on expert‐annotated data, offering a practical alternative when expert labelling is infeasible. While automated methods can be useful for data analysis, we caution against automatization of content filtering due to the inherent difficulty of CT detection and the potential biases of language models.

ABSTRACT This study investigates the robustness of Transformer models in irony detection addressing various textual perturbations, revealing potential biases in training data concerning ironic and non‐ironic classes. The perturbations involve three distinct approaches, each progressively increasing in complexity. The first approach is word masking, which employs wild‐card characters or utilises BERT‐specific masking through the mask token provided by BERT models. The second approach is word substitution, replacing the bias word with a contextually appropriate alternative. Lastly, paraphrasing generates a new phrase while preserving the original semantic meaning. We leverage Large Language Models (GPT 3.5 Turbo) and human inspection to ensure linguistic correctness and contextual coherence for word substitutions and paraphrasing. The results indicate that models are susceptible to these perturbations, and paraphrasing and word substitution demonstrate the most significant impact on model predictions. The irony class appears to be particularly challenging for models when subjected to these perturbations. The SHAP and LIME methods are used to correlate variations in attribution scores with prediction errors. A notable difference in the Total Variation of attribution scores is observed between original examples and cases involving bias word substitution or masking. Among the corpora used, TwSemEval2018 emerges as the most challenging. Regarding model performance, Transformer‐based models such as RoBERTa and BERTweet demonstrate superior overall performance addressing these perturbations. This research contributes to understanding the robustness and limitations of irony detection models, highlighting areas for improvement in model design and training data curation.

Depression has long been studied in the NLP field, with most works focusing on individuals’ negative emotions. People with depression experience happiness, but this has not been extensively studied. Previous works have shown that sentiment or emotion classification approaches are unsuitable for extracting happy moments because they may not be expressed only in positive words. In this work, we conduct a large-scale study of happy moments from social media texts of individuals mentioning a depression diagnosis. We develop an extensive deep learning-based framework to extract happy moments from text, and annotate them with semantic topics, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of users in the depression and control groups, varying by gender. We found that male and female users in the depression group expressed more sociality in their happy moments than control users. Furthermore, male users’ agency was not impaired in depression, while female users in the depression group expressed fewer happy moments with agency than the control group. Our research can inform psychology interventions, which can foster feelings of longer-lasting happiness and represent a promising path of collaboration between computational linguistics and psychology.

We introduce PyRater, an open-source Python toolkit designed for analysing corpora annotations. When creating new annotated language resources, probabilistic models of annotation are the state-of-the-art solution for identifying the best annotators, retrieving the gold standard, and more generally separating annotation signal from noise. PyRater offers a unified interface for several such models and includes an API for the addition of new ones. Additionally, the toolkit has built-in functions to read datasets with multiple annotations and plot the analysis outcomes. In this work, we also demonstrate a novel application of PyRater to zero-shot classifiers, where it effectively selects the best-performing prompt. We make PyRater available to the research community.

Recently, large language models (LLMs) have become increasingly powerful and have become capable of solving a plethora of tasks through proper instructions in natural language. However, the vast majority of testing suites assume that the instructions are written in English, the de facto prompting language. Code intelligence and problem solving still remain a difficult task, even for the most advanced LLMs. Currently, there are no datasets to measure the generalization power for code-generation models in a language other than English. In this work, we present RoCode, a competitive programming dataset, consisting of 2,642 problems written in Romanian, 11k solutions in C, C++ and Python and comprehensive testing suites for each problem. The purpose of RoCode is to provide a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text as well as a fine-tuning set for pretrained Romanian models. Through our results and review of related works, we argue for the need to develop code models for languages other than English.

Abstract The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding papers and conducted a thorough analysis, considering 18 criteria across four primary dimensions: availability details, content, annotation process, and reusability. This analysis enabled us to identify existing gaps and make recommendations for future research works. For the convenience of the research community, the list of the analysed datasets is maintained in a GitHub repository.

Abstract The current prevalence of conspiracy theories on the internet is a significant issue, tackled by many computational approaches. However, these approaches fail to recognize the relevance of distinguishing between texts which contain a conspiracy theory and texts which are simply critical and oppose mainstream narratives. Furthermore, little attention is usually paid to the role of inter‐group conflict in oppositional narratives. We contribute by proposing a novel topic‐agnostic annotation scheme that differentiates between conspiracies and critical texts, and that defines span‐level categories of inter‐group conflict. We also contribute with the multilingual XAI‐DisInfodemics corpus (English and Spanish), which contains a high‐quality annotation of Telegram messages related to COVID‐19 (5000 messages per language). We also demonstrate the feasibility of an NLP‐based automatization by performing a range of experiments that yield strong baseline solutions. Finally, we perform an analysis which demonstrates that the promotion of intergroup conflict and the presence of violence and anger are key aspects to distinguish between the two types of oppositional narratives, that is, conspiracy versus critical.

Abstract With this work, we hypothesize that semantically enriching a user's text corpus using backtranslation and expansion modules can improve performance for author profiling tasks. To perform this textual enrichment, we translate an author's representative text. Translations are made from one language—the source language—into another—the target language—and then back to the original one. Finally, we expand an author's text by integrating the original version with the back‐translated one. Our framework includes these backtranslation and expansion modules followed by a SOTA classifier successfully employed for text classification. The framework is tested on three author profiling datasets from the last three years’ shared tasks on fake news, hate speech, irony and stereotypes detection hosted at the CLEF conference for the PAN Lab. This work is an extension of our previous one where we just presented our main idea. Here we improve our framework, and we also investigate more languages and more datasets. Finally, a qualitative analysis is provided. The results confirm that the backtranslation and expansion add‐on modules improve model performance on all three datasets evaluated.

Abstract Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

In "Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation," published in Personal and Ubiquitous Computing 27 (1), 45-57, an oversight occurred regarding the clarity and comprehensibility of the proposed method.This erratum serves to rectify and clarify the methodological description to enhance readers' understanding of the research.

Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet$(h,r,t)$links two entities$h$and$t$through a relation$r$, oversimplifies the complex nature of the data stored in a KG, in particular for hyper-relational facts, where each fact contains not only a base triplet$(h,r,t)$, but also the associated key-value pairs$(k,v)$. Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. Moreover, as the KG schema information has been shown to be useful for resolving link prediction tasks, it is thus essential to incorporate the corresponding hyper-relational schema in KG embeddings. Against this background, we propose sHINGE, a schema-aware hyper-relational KG embedding model, which learns from hyper-relational facts directly (without the transformation to the n-ary representation) and their corresponding hyper-relational schema in a KG. Our extensive evaluation shows the superiority of sHINGE on various link prediction tasks over KGs. In particular, compared to a sizeable collection of 21 baselines, sHINGE consistently outperforms the best-performing triple-based KG embedding method, hyper-relational KG embedding method, and schema-aware KG embedding method by 19.1%, 1.8%, and 12.9%, respectively.

Modern Knowledge Graphs (KG) often suffer from an incompleteness issue (i.e., missing facts). By representing a fact as a triplet$(h,r,t)$linking two entities$h$and$t$via a relation$r$, existing KG completion approaches mostly consider a link prediction task to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as$(h,r,?)$. However, this task implicitly has a strong yet impractical assumption on the two given elements in a triplet, which have to be correlated, resulting otherwise in meaningless predictions, such as (Marie Curie,headquarters location, ?). Against this background, this paper studies an instance completion task suggesting$r$-$t$pairs for a given$h$, i.e.,$(h,?,?)$. Inspired by the human psychological principle “fast-and-slow thinking”, we propose a two-step schema-aware approach RETA++ to efficiently solve our instance completion problem. It consists of two components: afastRETA-Filter efficiently filtering candidate$r$-$t$pairs schematically matching the given$h$, and adeliberateRETA-Grader leveraging a KG embedding model scoring each candidate$r$-$t$pair considering the plausibility of both the input triplet and its corresponding schema. RETA++ systematically integrates them by training RETA-Grader on the reduced solution space output by RETA-Filter via a customized negative sampling process, so as to fully benefit from the efficiency of RETA-Filter in solution space reduction and the deliberation of RETA-Grader in scoring candidate triplets. We evaluate our approach against a sizable collection of state-of-the-art techniques on three real-world KG datasets. Results show that RETA-Filter can efficiently reduce the solution space for the instance completion task, outperforming best baseline techniques by 10.61%–84.75% on the reduced solution space size, while also being 1.7×–29.6x faster than these techniques. Moreover, RETA-Grader trained on the reduced solution space also significantly outperforms the best state-of-the-art techniques on the instance completion task by 31.90%–105.02%.

Cross-lingual transfer learning from highresource to medium and low-resource languages has shown encouraging results.However, the scarcity of resources in target languages remains a challenge.In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.For data augmentation, we analyze two existing techniques based on vicinal risk minimization and propose MIXAG, a novel data augmentation method which interpolates pairs of instances based on the angle of their representations.Our experiments involve seven languages typologically distinct from English and three different domains.The results reveal that the data augmentation strategies can enhance fewshot cross-lingual abusive language detection.Specifically, we observe that consistently in all target languages, MIXAG improves significantly in multidomain and multilingual environments.Finally, we show through an error analysis how the domain adaptation can favour the class of abusive texts (reducing false negatives), but at the same time, declines the precision of the abusive language detection model.

This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of computational resources, making this solution unfeasible in cases where data are unavailable or expensive to gather. Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-task Learning (MTL). Despite the recent developments regarding MTL, the problem of negative transfer has still to be solved. Negative transfer is a phenomenon that occurs when noisy information is shared between tasks, resulting in a drop in performance. This paper proposes a new approach to mitigate the negative transfer problem based on the task awareness concept. The proposed approach results in diminishing the negative transfer together with an improvement of performance over classic MTL solution. Moreover, the proposed approach has been implemented in two unified architectures to detect Sexism, Hate Speech, and Toxic Language in text comments. The proposed architectures set a new state-of-the-art both in EXIST-2021 and HatEval-2019 benchmarks.

Warning: This paper contains examples of language and images which may be offensive. Misogyny is a form of hate against women and has been spreading exponentially through the Web, especially on social media platforms. Hateful content towards women can be conveyed not only by text but also using visual and/or audio sources or their combination, highlighting the necessity to address it from a multimodal perspective. One of the predominant forms of multimodal content against women is represented by memes, which are images characterized by pictorial content with an overlaying text introduced a posteriori. Its main aim is originally to be funny and/or ironic, making misogyny recognition in memes even more challenging. In this paper, we investigated 4 unimodal and 3 multimodal approaches to determine which source of information contributes more to the detection of misogynous memes. Moreover, a bias estimation technique is proposed to identify specific elements that compose a meme that could lead to unfair models, together with a bias mitigation strategy based on Bayesian Optimization. The proposed method is able to push the prediction probabilities towards the correct class for up to 61.43% of the cases. Finally, we identified the most challenging archetypes of memes that are still far to be properly recognized, highlighting the most relevant open research directions.

Hate speech detection refers broadly to the automatic identification of language that may be considered discriminatory against certain groups of people. The goal is to help online platforms to identify and remove harmful content. Humans are usually capable of detecting hatred in critical cases, such as when the hatred is non-explicit, but how do computer models address this situation? In this work, we aim to contribute to the understanding of ethical issues related to hate speech by analysing two transformer-based models trained to detect hate speech. Our study focuses on analysing the relationship between these models and a set of hateful keywords extracted from the three well-known datasets. For the extraction of the keywords, we propose a metric that takes into account the division among classes to favour the most common words in hateful contexts. In our experiments, we first compared the overlap between the extracted keywords with the words to which the models pay the most attention in decision-making. On the other hand, we investigate the bias of the models towards the extracted keywords. For the bias analysis, we characterize and use two metrics and evaluate two strategies to try to mitigate the bias. Surprisingly, we show that over 50% of the salient words of the models are not hateful and that there is a higher number of hateful words among the extracted keywords. However, we show that the models appear to be biased towards the extracted keywords. Experimental results suggest that fitting models with hateful texts that do not contain any of the keywords can reduce bias and improve the performance of the models.

Abstract Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims’ life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.

Abstract The proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.

Graph embeddings have been widely used for many graph analysis tasks. Mainstream factorization-based and graph-sampling-based embedding learning schemes both involve many hyperparameters and design choices. However, existing techniques often adopt some heuristics for these hyperparameters and design choices with little investigation into their impact, making it unclear what is the exact performance gains of these techniques on graph analysis tasks. Against this background, this paper presents a systematic study on the impact of an extensive list of hyperparameters for both factorization-based and graph-sampling-based graph embedding techniques for homogeneous graphs. We design generalized factorization-based and graph-sampling-based techniques involving these hyperparameters, and conduct a comprehensive set of experiments with over 3,000 embedding models trained and evaluated per dataset. We reveal that much of the performance gains are indeed due to optimal hyperparameter settings/design choices rather than the sophistication of embedding models; appropriate hyperparameter settings for typical embedding techniques can outperform a sizeable collection of 18 state-of-the-art graph embedding techniques by 0.30-35.41% across different tasks. Moreover, we find that there is no one-size-fits-all hyperparameter setting across tasks, but we can indeed provide a list of task-specific practical recommendations for these hyperparameter settings/design choices, which we believe can serve as important guidelines for future research on embedding based graph analyses.

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes, beyond the users’ binary labels, also their fine-grained credibility level (very low to very high) and their political bias strength (extreme right to extreme left). As far as we are aware, this is the first fake news spreader dataset that simultaneously captures both the long-term context of users’ historical posts and the interactions between them. To create the first benchmark on our data, we provide methods for identifying misinformation spreaders by utilizing the social connections between the users along with their psycho-linguistic features. We show that the users’ social interactions can, on their own, indicate misinformation spreading, while the psycho-linguistic features are mostly informative in non-neural classification settings. In a qualitative analysis we observe that detecting affective mental processes correlates negatively with right-biased users, and that the openness to experience factor is lower for those who spread fake news.

Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. Hence, many models suffer from the generalization problem. However, retrieving and monitoring hateful content on social media is a current necessity. In this paper, we propose an unsupervised approach using Graph Auto-Encoders (GAE), which allows us to avoid using labeled data when training the representation of the texts. Specifically, we represent texts as nodes of a graph, and use a transformer layer together with a convolutional layer to encode these nodes in a low-dimensional space. As a result, we obtain embeddings that can be decoded into a reconstruction of the original network. Our main idea is to learn a model with a set of texts without supervision, in order to generate embeddings for the nodes: nodes with the same label should be close in the embedding space, which, in turn, should allow us to distinguish among classes. We employ this strategy to detect hate speech in multi-domain and multilingual sets of texts, where our method shows competitive results on small datasets.

Mental disorders are a serious and increasingly relevant public health issue. NLP methods have the potential to assist with automatic mental health disorder detection, but building annotated datasets for this task can be challenging; moreover, annotated data is very scarce for disorders other than depression. Understanding the commonalities between certain disorders is also important for clinicians who face the problem of shifting standards of diagnosis. We propose that transfer learning with linguistic features can be useful for approaching both the technical problem of improving mental disorder detection in the context of data scarcity, and the clinical problem of understanding the overlapping symptoms between certain disorders. In this paper, we target four disorders: depression, PTSD, anorexia and self-harm. We explore multi-aspect transfer learning for detecting mental disorders from social media texts, using deep learning models with multi-aspect representations of language (including multiple types of interpretable linguistic features). We explore different transfer learning strategies for cross-disorder and cross-platform transfer, and show that transfer learning can be effective for improving prediction performance for disorders where little annotated data is available. We offer insights into which linguistic features are the most useful vehicles for transferring knowledge, through ablation experiments, as well as error analysis.

Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso, Vikram Nanda, Sudheer Chava. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users’ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words.

Ad-hoc table retrieval, also known as table search, is the problem of finding tables relevant to a search query. This search query can be a keyword or a table itself, referred to as keyword-based and table-based search, respectively. With the vast amounts of tabular data available online, it has become essential for users to identify relevant tables that meet their search criteria. In this regard, there has been a wide variety of research on this problem using pure lexical features, semantic representation, embeddings, as well as intrinsic and extrinsic features of the tables. However, one of the significant limitations of most of the existing methods is that they do not keep the table’s structure and the globalized context intact when building semantic representations of tabular data. Deriving motivation from this fact, we propose an effective approach based on Convolutional Neural Networks (CNNs) – ConvTab – to train the embeddings of tabular data. Our approach is divided into two phases. First, we leverage the discriminating power of CNNs to train a table classifier. Next, the representations learned from this model are used to generate semantic features for query-table similarity. These query-table similarity features are then used as input to the learning algorithm. We evaluate our approach on the table retrieval task using standard NDCG, MAP, and MRR metrics. Experiments reveal that ConvTab significantly outperforms the state of the art in ad-hoc table retrieval by 16.9% and 8.37% using NDCG at cutoffs 5 and 20, respectively. For reproducibility purposes, we share our model as well as all details of our implementation1.

Fake news articles often stir the readers' attention by means of emotional appeals that arouse their feelings.Unlike in short news texts, authors of longer articles can exploit such affective factors to manipulate readers by adding exaggerations or fabricating events, in order to affect the readers' emotions.To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture.The proposed model, FakeFlow, learns this flow by combining topic and affective information extracted from text.We evaluate the model's performance with several experiments on four real-world datasets.The results show that FakeFlow achieves superior results when compared against state-ofthe-art methods, thus confirming the importance of capturing the flow of the affective information in news articles.

Knowledge Graph (KG) completion has been widely studied to tackle the incompleteness issue (i.e., missing facts) in modern KGs. A fact in a KG is represented as a triplet (h, r, t) linking two entities h and t via a relation r. Existing work mostly consider link prediction to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (h, r, ?). This task has, however, a strong assumption on the two given elements in a triplet, which have to be correlated, resulting otherwise in meaningless predictions, such as (Marie Curie, headquarters location, ?). In addition, the KG completion problem has also been formulated as a relation prediction task, i.e., when predicting relations r for a given entity h. Without predicting t, this task is however a step away from the ultimate goal of KG completion. Against this background, this paper studies an instance completion task suggesting r-t pairs for a given h, i.e., (h, ?, ?). We propose an end-to-end solution called RETA (as it suggests the Relation and Tail for a given head entity) consisting of two components: a RETA-Filter and RETA-Grader. More precisely, our RETA-Filter first generates candidate r-t pairs for a given h by extracting and leveraging the schema of a KG; our RETA-Grader then evaluates and ranks the candidate r-t pairs considering the plausibility of both the candidate triplet and its corresponding schema using a newly-designed KG embedding model. We evaluate our methods against a sizable collection of state-of-the-art techniques on three real-world KG datasets. Results show that our RETA-Filter generates of high-quality candidate r-t pairs, outperforming the best baseline techniques while reducing by 10.61%-84.75% the candidate size under the same candidate quality guarantees. Moreover, our RETA-Grader also significantly outperforms state-of-the-art link prediction techniques on the instance completion task by 16.25%-65.92% across different datasets.

Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.

Fake news is considered one of the main threats of our society. The aim of fake news is usually to confuse readers and trigger intense emotions to them in an attempt to be spread through social networks. Even though recent studies have explored the effectiveness of different linguistic patterns for fake news detection, the role of emotional signals has not yet been explored. In this paper, we focus on extracting emotional signals from claims and evaluating their effectiveness on credibility assessment. First, we explore different methodologies for extracting the emotional signals that can be triggered to the users when they read a claim. Then, we present emoCred, a model that is based on a long-short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. In addition, we perform an analysis to understand which emotional signals and which terms are the most useful for the different credibility classes. We conduct extensive experiments and a thorough analysis on real-world datasets. Our results indicate the importance of incorporating emotional signals in the credibility assessment problem.

Social media have given the opportunity to users to express their opinions online in a fast and easy way. The ease of generating content online and the anonymity that social media provide have increased the amount of harmful content that is published. This tutorial will focus on the topic of online harmful information. First, we will analyse and explain the different types of online harmful information with a particular focus on fake news and hate speech. In addition, we will explain the different computational approaches proposed in the literature for the detection of fake news and hate speech. Next, we will present details regarding the evaluation process, datasets and shared tasks and finally, we will discuss future directions in the field of online harmful information detection.

This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian).It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme.Three distinct experimental settings are provided.In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored.In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets.In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture.The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.

Recent years have seen a large increase in the amount of false information that is posted online. Fake news are created and propagated in order to deceive users and manipulate opinions and subsequently have a negative impact on the society. The automatic detection of fake news is very challenging since some of those news are created in sophisticated ways containing text or images that have been deliberately modified. Combining information from different modalities can be very useful for determining which of the online articles are fake. In this paper, we propose a multimodal multi-image system that combines information from different modalities in order to detect fake news posted online. In particular, our system combines textual, visual and semantic information. For the textual representation we use the Bidirectional Encoder Representations from Transformers (BERT) to better capture the underlying semantic and contextual meaning of the text. For the visual representation we extract image tags from multiple images that the articles contain using the VGG-16 model. The semantic representation refers to the text-image similarity calculated using the cosine similarity between the title and image tags embeddings. Our experimental results on a real world dataset show that combining features from the different modalities is effective for fake news detection. In particular, our multimodal multi-image system significantly outperforms the BERT baseline by 4.19% and SpotFake by 5.39%.

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The most adopted scheme is to incorporate spatiotemporal factors into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme oversimplifies the temporal periodicity and spatial regularity of user mobility, and thus cannot fully benefit from rich historical spatiotemporal contexts encoded in user mobility traces. Against this background, we propose Flashback, a general RNN architecture designed for modeling sparse user mobility traces by doing flashbacks on hidden states in RNNs. Specifically, Flashback explicitly uses spatiotemporal contexts to search past hidden states with high predictive power (i.e., historical hidden states sharing similar contexts as the current one) for location prediction, which can then directly benefit from rich spatiotemporal contexts. Our extensive evaluation compares Flashback against a sizable collection of state-of-the-art techniques on two real-world LBSN datasets. Results show that Flashback consistently and significantly outperforms state-of-the-art RNNs involving spatiotemporal factors by 15.9% to 27.6% in the next location prediction task.

The recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features. As our case study we therefore introduce a recently released Universal Dependencies treebank for Italian which includes ironic tweets: TWITTIRÒ-UD. For the purposes of this study, we enriched the existing annotation in the treebank, with a further level that includes irony activators. A description and discussion of the annotation scheme is provided with a definition of irony activators and the guidelines for their annotation. This qualitative study on the different layers of annotation applied on the same dataset can shed some light on the process of human annotation, and irony annotation in particular, and on the usefulness of this representation for developing computational models of irony to be used for training purposes.

Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts. Given the difficulty of the task, several fact checking websites have been developed to raise awareness about which articles contain fabricated information. As a result of those platforms, several users are interested to share posts that cite evidence with the aim to refute fake news and warn other users. These users are known as fact checkers . However, there are users who tend to share false information, who can be characterised as potential fake news spreaders . In this paper, we propose the CheckerOrSpreader model that can classify a user as a potential fact checker or a potential fake news spreader. Our model is based on a Convolutional Neural Network (CNN) and combines word embeddings with features that represent users’ personality traits and linguistic patterns used in their tweets. Experimental results show that leveraging linguistic patterns and personality traits can improve the performance in differentiating between checkers and spreaders.

Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs. Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) links two entities h and t through a relation r, and learn entity/relation embeddings from such triplets while preserving such a structure. However, this triplet representation oversimplifies the complex nature of the data stored in the KG, in particular for hyper-relational facts, where each fact contains not only a base triplet (h, r, t), but also the associated key-value pairs (k, v). Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG. HINGE captures not only the primary structural information of the KG encoded in the triplets, but also the correlation between each triplet and its associated key-value pairs. Our extensive evaluation shows the superiority of HINGE on various link prediction tasks over KGs. In particular, HINGE consistently outperforms not only the KG embedding methods learning from triplets only (by 0.81-41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2-84.1%).

Abstract The rise of social media empowers people to interact and communicate with anyone anywhere in the world. The possibility of being anonymous avoids censorship and enables freedom of expression. Nevertheless, this anonymity might lead to cybersecurity issues, such as opinion spam, sexual harassment, incitement to hatred or even terrorism propaganda. In such cases, there is a need to know more about the anonymous users and this could be useful in several domains beyond security and forensics such as marketing, for example. In this paper, we focus on a fine-grained analysis of language varieties while considering also the authors’ demographics. We present a Low-Dimensionality Statistical Embedding method to represent text documents. We compared the performance of this method with the best performing teams in the Author Profiling task at PAN 2017. We obtained an average accuracy of 92.08% versus 91.84% for the best performing team at PAN 2017. We also analyse the relationship of the language variety identification with the authors’ gender. Furthermore, we applied our proposed method to a more fine-grained annotated corpus of Arabic varieties covering 22 Arab countries and obtained an overall accuracy of 88.89%. We have also investigated the effect of the authors’ age and gender on the identification of the different Arabic varieties, as well as the effect of the corpus size on the performance of our method.

International audience

Fake news is risky, since it has been created to manipulate readers’ opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news article sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed an LSTM neural network model that is emotionally infused to detect false news.

Jointly learning embeddings from text and a Knowledge Graph benefits both word and entity/relation embeddings by taking advantage of both large-scale unstructured content (text) and high-quality structured data (the Knowledge Graph). Current techniques leverage anchors to associate entities in the Knowledge Graph to corresponding words in the text corpus; these anchors are then used to generate additional learning samples during the embedding learning process. However, we show in this paper that such techniques yield suboptimal results, as they fail to control the amount of shared information between the two data sources during the joint learning process. Moreover, the additional learning samples often incur significant computational overhead. Aiming at releasing the power of such joint embeddings, we propose JOINER, a new joint text and Knowledge Graph embedding method using regularization. JOINER not only preserves co-occurrence between words in a text corpus and relations between entities in a Knowledge Graph, it also provides the flexibility to control the amount of information shared between the two data sources via regularization. Our method does not generate additional learning samples, which makes it computationally efficient. Our extensive empirical evaluation on real datasets shows the superiority of JOINER across different evaluation tasks, including analogical reasoning, link prediction, and relation extraction. Compared to state-of-the-art techniques generating additional learning samples from a set of anchors, our method yields better results (with up to 4.3% absolute improvement) and significantly less computational overhead (76% less learning time overhead).

In the framework of the QNRF project on Arabic Author Profiling for Cyber-Security, we addressed deception detection in Arabic in order to discard those messages that do not really represent potential threats. We have applied the Low Dimensionality Statistical Embedding (LDSE) method to several corpora for Arabic including the Arabic credibility corpus and two new corpora that we created: the Qatar Twitter corpus and the Qatar News corpus. We achieved a performance of 0.797 Macro F-measure on the Arabic Credibility corpus. The obtained results with two well-known distributed representations, namely Continuous Bag of Words and Skip Grams, showed the competitiveness of our approach. The LDSE approach gave similar results on the two corpora that we created. We evaluated our work in a cross-genre scenario, showing the robustness of LDSE when there are enough data about similar topics.

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques usually require significant computational resources for the learning process, which hinders their applications on large-scale graphs. Moreover, the cosine similarity preserved by these techniques shows suboptimal efficiency in downstream graph analysis tasks, compared to Hamming similarity, for example. To address these issues, we propose NodeSketch, a highly-efficient graph embedding technique preserving high-order node proximity via recursive sketching. Specifically, built on top of an efficient data-independent hashing/sketching technique, NodeSketch generates node embeddings in Hamming space. For an input graph, it starts by sketching the self-loop-augmented adjacency matrix of the graph to output low-order node embeddings, and then recursively generates k-order node embeddings based on the self-loop-augmented adjacency matrix and (k-1)-order node embeddings. Our extensive evaluation compares NodeSketch against a sizable collection of state-of-the-art techniques using five real-world graphs on two graph analysis tasks. The results show that NodeSketch achieves state-of-the-art performance compared to these techniques, while showing significant speedup of 9x-372x in the embedding learning process and 1.19x-1.68x speedup when performing downstream graph analysis tasks.

The spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.

Cross language plagiarism is the unacknowledged reuse of text across language pairs. It occurs if a passage of text is translated from source language to target language and no proper citation is provided. Although various methods have been developed for detection of cross language plagiarism, less attention has been paid to measure and compare their performance, especially when tackling with different types of paraphrasing through translation. In this paper, we investigate various approaches to cross language plagiarism detection. Moreover, we present a novel approach to cross language plagiarism detection using word embedding methods and explore its performance against other state-of-the-art plagiarism detection algorithms. In order to evaluate the methods, we have constructed an English-Persian bilingual plagiarism detection corpus (referred to as HAMTA-CL) comprised of seven types of obfuscation. The results show that the word embedding approach outperforms the other approaches with respect to recall when encountering heavily paraphrased passages. On the other hand, translation based approach performs well when the precision is the main consideration of the cross language plagiarism detection system.

The objective of Native Language Identification is to determine the native language of the author of a text that he or she wrote in another language. By contrast, Language Variety Identification aims at classifying texts representing different varieties of a single language. We postulate that both tasks may be reduced to a single objective, which is to identify the language variety of the text. We design a general approach that combines string kernels and word embeddings, which capture different characteristics of texts. The results of our experiments show that the approach achieves excellent results on both tasks, without any task-specific adaptations.

Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation.

[EN] Today¿s Internet is evolving toward an open society of humans and computational\nentities, where intelligent agent systems increasingly support the interaction between\nusers and computational components. In this scenario, affect plays a key role, with functions\nthat span from creating and maintaining interpersonal relations, to establishing\ncooperation and trust. Artificial systems¿which can act as both actors and facilitators\nof these interactions¿are more and more requested to integrate affective components\nin order to achieve truly realistic social behaviors and foster the creation of bonds with\nthe users, with timely reactions to their affective input and appropriate expressions\nof affect. Achieving this integration requires to understand and reproduce the role of\naffect in human expressive capabilities, and to account for affect-related phenomena\n(e.g., sentiment, emotions, and mood) that engage social abilities, such as empathy,\nand expressive means, such as irony. The expectation for complex phenomena such as\nempathy and irony is that effective approaches require an interdisciplinary approach\nand, above all, the integration of representational models and data-oriented processing\ntechniques. The aim of this special section is to bring together leading research\non computational models of affect-related phenomena in interactions occurring either\nin social media or agent-based systems, by attaining cross-fertilization between two\nrelevant perspectives: on the one hand, research on agent architectures and cognitive models, mainly concerned with the integration of affective states into agents and open\nto the creation of virtual and embodied agents; on the other hand, research on techniques\nfor sentiment analysis and opinion mining, mainly focused on the processing\nof affective information in social media, typically (but not only) expressed through\ntext. The integration of models and methods between the two perspectives can open\nthe way to the development of a new generation of social and interactive applications\nthat leverage the affective dimension to promote improved, spontaneous technologymediated\ninteractions, including human¿computer and human¿human interactions,\non small and large scale.\nOur goal here is to provide an overview of the open research challenges for the\ncommunity of researchers interested in analyzing and modeling the interplay between\naffect and interaction in agent-based systems and social media, as well as an introduction\nto the special section. The rest of the article is structured as follows. Section 2\ndiscusses a set of research challenges relevant in the context of this special issue,\nSection 3 briefly introduces the articles included in this ACM TOIT special section.\nSection 4 concludes the article.

The Arabic WordNet project has provided the Arabic Natural Language Processing (NLP) community with the first WordNet-compliant resource.It allowed new possibilities in terms of building sophisticated NLP applications related to this Semitic language.In this paper, we present the new content added to this resource, using semi-automatic techniques, and validated by Arabic native-speaker lexicographers.We also present how this content helps in the implementation of new Arabic NLP applications, especially for Question Answering (QA) systems.The obtained results show the contribution of the added content.The resource, fully transformed into the standard Lexical Markup Framework (LMF), is made available for the community.

In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discus sed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.

The vast amount of scientific publications available online makes it easier for students and researchers to reuse text from other authors and makes it harder for checking the originality of a given text. Reusing text without crediting the original authors is considered plagiarism. A number of studies have reported the prevalence of plagiarism in academia. As a consequence, numerous institutions and researchers are dedicated to devising systems to automate the process of checking for plagiarism. This work focuses on the problem of detecting text reuse in scientific papers. The contributions of this paper are twofold: (a) we survey the existing approaches for plagiarism detection based on content, based on content and structure, and based on citations and references; and (b) we compare content and citation‐based approaches with the goal of evaluating whether they are complementary and if their combination can improve the quality of the detection. We carry out experiments with real data sets of scientific papers and concluded that a combination of the methods can be beneficial.

Abstract This article presents silhouette–attraction (Sil–Att), a simple and effective method for text clustering, which is based on two main concepts: thesilhouette coefficientand the idea ofattraction. The combination of both principles allows us to obtain a general technique that can be used either as a boosting method, which improves results of other clustering algorithms, or as an independent clustering algorithm. The experimental work shows thatSil–Attis able to obtain high-quality results on text corpora with very different characteristics. Furthermore, its stable performance on all the considered corpora is indicative that it is a very robust method. This is a very interesting positive aspect ofSil–Attwith respect to the other algorithms used in the experiments, whose performances heavily depend on specific characteristics of the corpora being considered.

Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.

Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.

Current approaches to cross-language doc-ument retrieval and categorization are based on discriminative methods which represent documents in a low-dimensional vector space. In this paper we pro-pose a shift from the supervised to the knowledge-based paradigm and provide a document similarity measure which draws on BabelNet, a large multilingual knowl-edge resource. Our experiments show state-of-the-art results in cross-lingual document retrieval and categorization. 1

In this paper, a new modeling and learning approach is presented which is based on two assumptions from the field of psychology: 1. The number of Tweets mainly depends on previous dynamics of the discussion, i.e. a state-space modeling approach is used for the first time. 2. Humans mainly react to emotional stimuli, i.e. Tweets are automatically characterized by their emotional content. Therefore, the emotions of conversations are extracted and used for system identification and parameter estimation of a state space model, which deals with events and its transitions.

When it is not possible to compare the suspi-cious document to the source document(s) plagiarism has been committed from, the evi-dence of plagiarism has to be looked for in-trinsically in the document itself. In this pa-per, we introduce a novel language-independent intrinsic plagiarism detection method which is based on a new text repre-sentation that we called n-gram classes. The proposed method was evaluated on three pub-licly available standard corpora. The obtained results are comparable to the ones obtained by the best state-of-the-art methods. 1

For many languages that use non-Roman based indigenous scripts (e.g., Arabic, Greek and Indic languages) one can often find a large amount of user generated transliterated content on the Web in the Roman script. Such content creates a monolingual or multi-lingual space with more than one script which we refer to as the Mixed-Script space. IR in the mixed-script space is challenging because queries written in either the native or the Roman script need to be matched to the documents written in both the scripts. Moreover, transliterated content features extensive spelling variations. In this paper, we formally introduce the concept of Mixed-Script IR, and through analysis of the query logs of Bing search engine, estimate the prevalence and thereby establish the importance of this problem. We also give a principled solution to handle the mixed-script term matching and spelling variation where the terms across the scripts are modelled jointly in a deep-learning architecture and can be compared in a low-dimensional abstract space. We present an extensive empirical analysis of the proposed method along with the evaluation results in an ad-hoc retrieval setting of mixed-script IR where the proposed method achieves significantly better results (12% increase in MRR and 29% increase in MAP) compared to other state-of-the-art baselines.

This paper describes a new tool that helps extracting clean text from the Arabic Wikisource dump in order to build corpora. The tool purpose is illustrated by the generation of a subcorpus from Wikisource, which is a step towards the building of an evaluation corpus for Arabic intrinsic plagiarism detection.

Like most of the languages which have only recently started being investigated for the Natural Language Processing (NLP) tasks, Amazigh lacks annotated corpora and tools and still suffers from the scarcity of linguistic tools and resources. The main aim of this paper is to present a tokenizer tool and a new part-of-speech (POS) tagger based on a new Amazigh tag set (AMTS) composed of 28 tag. In line with our goal we have trained two sequence classification models using Support Vector Machines (SVMs) and Conditional Random Fields (CRFs) to build a toknizer and a POS tagger for the Amazigh language. We have used the 10-fold technique to evaluate and validate our approach. We report that POS tagging results using SVMs and CRFs are very comparable. Across the board, CRFs outperformed SVMs on the fold level (91.18% vs. 90.75%) and CRFs outperformed SVMs on the 10 folds average level (87.95% vs. 87.11%). Regarding tokenization task, SVMs outperformed CRFs on the fold level (99.97% vs. 99.85%) and on the 10 folds average level (99.95% vs. 99.89%).

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.

Subjective language contains information about private states. The goal of subjective language identification is to determine that a private state is expressed, without considering its polarity or specific emotion. A component of word meaning, “Personal Sense,” has clear potential in the field of subjective language identification, as it reflects a meaning of words in terms of unique personal experience and carries personal characteristics. In this paper we investigate how Personal Sense can be harnessed for the purpose of identifying subjectivity in news titles. In the process, we develop a new Personal Sense annotation framework for annotating and classifying subjectivity, polarity, and emotion. The Personal Sense framework yields high performance in a fine‐grained subsentence subjectivity classification. Our experiments demonstrate lexico‐syntactic features to be useful for the identification of subjectivity indicators and the targets that receive the subjective Personal Sense.

The primary goal of this special section of ACM Transactions on Intelligent Systems and Technology is to foster research in the interplay between Social Media, Data/Opinion Mining and Search, aiming to reflect the actual developments in technologies that exploit user-generated content.

Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed.

In this paper, we provide an overview of the 3rd International Workshop on Search and Mining User-generated Contents, held in conjunction with the 20th ACM International Conference on Information and Knowledge Management. We present the motivation and goals of the workshop, and some statistics and details about accepted papers and keynotes.

Clustering narrow domain short texts is considered to be a complex task because of the intrinsic features of the corpus to be clustered: (i) the low frequencies of vocabulary terms in short texts, and (ii) the high vocabulary overlapping associated to narrow domains. The aim of this paper is to introduce a self-term expansion methodology for improving the performance of clustering methods when dealing with corpora of this kind. This methodology allows raw textual data to be enriched by adding co-related terms from an automatically constructed lexical knowledge resource obtained from the same target data set (and not from an external resource). We also propose a set of supervised and unsupervised text assessment measures for evaluating different corpus features, such as shortness, stylometry and domain broadness. With the help of these measures, we may determine beforehand whether or not to use the methodology proposed in this paper. Finally, we integrate all these assessment measures in a freely available web-based system named Watermarking Corpora On-line System, which may be used by computer scientists in order to evaluate the different features associated with a given textual corpus.

This overview introduces the aim of the SMUC 2010 workshop, as well as the list of papers presented in the workshop.

“Ontology matching” is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our “structural matching method via upper ontology” uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our “nonstructural matching method” via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The “mixed method” that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.

The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact of the different features in isolation and incrementally combine them in order to evaluate the robustness to noise of each approach. We achieve the highest performance using a combination of 15 features in conditional random fields using broadcast news data (Fbeta=1=83.34).

Nowadays, a huge quantity of information is stored in digital format. A great portion of this information is constituted by textual and unstructured documents, where geographical references are usually given by means of place names. A common problem with textual information retrieval is represented by polysemous words, that is, words can have more than one sense. This problem is present also in the geographical domain: place names may refer to different locations in the world. In this paper we investigate the use of our word sense disambiguation technique in the geographical domain, with the aim of resolving ambiguous place names. Our technique is based on WordNet conceptual density. Due to the lack of a reference corpus tagged with WordNet senses, we carried out the experiments over a set of 1,210 place names extracted from the SemCor corpus that we named GeoSemCor and made publicly available. We compared our method with the most‐frequent baseline and the enhanced‐Lesk method, which previously has not been tested in large contexts. The results show that a better precision can be achieved by using a small context (phrase level), whereas a greater coverage can be obtained by using large contexts (document level). The proposed method should be tested with other corpora, due to the fact that our experiments evidenced the excessive bias towards the most‐frequent sense of the GeoSemCor.