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This paper introduces the HTEC HindiTerm Extraction Dataset 2.0, a resourcedesigned to support terminology extractionand classification tasks within the education domain. HTEC 2.0 has been developed with the objective of providing a high-quality benchmark dataset for the evaluation of term recognition and classification methodologies in Hindi educationaldiscourse. The dataset consists of 97 documents sourced from Hindi Wikipedia, covering a diverse range of topics relevant tothe education sector. Within these documents, 1,702 terms have been manuallyannotated where each term is defined as asingle-word or multi-word expression thatconveys a domain-specific meaning. Theannotated terms in HTEC 2.0 are systematically categorized into seven distinct classes.Furthermore, this paper outlines the development of annotation guidelines, detailingthe criteria used to determine term boundaries and category assignments. By offeringa structured dataset with clearly definedterm classifications, HTEC 2.0 serves as avaluable resource for researchers workingon terminology extraction, domain-specificnamed entity recognition, and text classification in Hindi.

The increasing size and diversity of corpora in natural language processing requires highly efficient processing frameworks. Building on the universal corpus format, Teanga, we present Cuaċ, a format for the compact representation of corpora. We describe this methodology based on short-string compression and indexing techniques and show that the files created with this methodology are similar to compressed human-readable serializations and can be further compressed using lossless compression. We also show that this introduces no computational penalty on the time to process files. This methodology aims to speed up natural language processing pipelines and is the basis for a fast database system for corpora.

The proliferation of misinformation necessitates robust yet computationally efficient fact verification systems. While current state-of-the-art approaches leverage Large Language Models (LLMs) for generating explanatory rationales, these methods face significant computational barriers and hallucination risks in real-world deployments. We present DeReC (Dense Retrieval Classification), a lightweight framework that demonstrates how general-purpose text embeddings can effectively replace autoregressive LLM-based approaches in fact verification tasks. By combining dense retrieval with specialized classification, our system achieves better accuracy while being significantly more efficient. DeReC outperforms explanation-generating LLMs in efficiency, reducing runtime by 95% on RAWFC (23 minutes 36 seconds compared to 454 minutes 12 seconds) and by 92% on LIAR-RAW (134 minutes 14 seconds compared to 1692 minutes 23 seconds), showcasing its effectiveness across varying dataset sizes. On the RAWFC dataset, DeReC achieves an F1 score of 65.58%, surpassing the state-of-the-art method L-Defense (61.20%). Our results demonstrate that carefully engineered retrieval-based systems can match or exceed LLM performance in specialized tasks while being significantly more practical for real-world deployment.

Personalized recommender systems play a crucial role in direct marketing, particularly in financial services, where delivering relevant content can enhance customer engagement and promote informed decision-making. This study explores interpretable knowledge graph (KG)-based recommender systems by proposing two distinct approaches for personalized article recommendations within a multinational financial services firm. The first approach leverages Reinforcement Learning (RL) to traverse a KG constructed from both structured (tabular) and unstructured (textual) data, enabling interpretability through Path Directed Reasoning (PDR). The second approach employs the XGBoost algorithm, with post-hoc explainability techniques such as SHAP and ELI5 to enhance transparency. By integrating machine learning with automatically generated KGs, our methods not only improve recommendation accuracy but also provide interpretable insights, facilitating more informed decision-making in customer relationship management.

The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixing and a statistical study to understand the language preferences of speakers with different polarities. With these analyses, we also train baseline models to evaluate the dataset’s quality.

Word representations like GloVe and Word2Vec encapsulate semantic and syntactic attributes and constitute the fundamental building block in diverse Natural Language Processing (NLP) applications. Such vector embeddings are typically stored in float32 format, and for a substantial vocabulary size, they impose considerable memory and computational demands due to the resource-intensive float32 operations. Thus, representing words via binary embeddings has emerged as a promising but challenging solution. In this paper, we introduce BRECS, an autoencoder-based Siamese framework for the generation of enhanced binary word embeddings (from the original embeddings). We propose the use of the novel Binary Cosine Similarity (BCS) regularisation in BRECS, which enables it to learn the semantics and structure of the vector space spanned by the original word embeddings, leading to better binary representation generation. We further show that our framework is tailored with independent parameters within the various components, thereby providing it with better learning capability. Extensive experiments across multiple datasets and tasks demonstrate the effectiveness of BRECS, compared to existing baselines for static and contextual binary word embedding generation. The source code is available at https://github.com/rajbsk/brecs.

Text style transfer (TST) involves altering the linguistic style of a text while preserving its style-independent content.This paper focuses on sentiment transfer, a popular TST subtask, across a spectrum of Indian languages: Hindi, Magahi, Malayalam, Marathi, Punjabi, Odia, Telugu, and Urdu, expanding upon previous work on English-Bangla sentiment transfer (Mukherjee et al., 2023a).We introduce dedicated datasets of 1,000 positive and 1,000 negative style-parallel sentences for each of these eight languages.We then evaluate the performance of various benchmark models categorized into parallel, non-parallel, cross-lingual, and shared learning approaches, including the Llama2 and GPT-3.5 large language models (LLMs).Our experiments highlight the significance of parallel data in TST and demonstrate the effectiveness of the Masked Style Filling (MSF) approach (Mukherjee et al., 2023a) in non-parallel techniques.Moreover, crosslingual and joint multilingual learning methods show promise, offering insights into selecting optimal models tailored to the specific language and task requirements.To the best of our knowledge, this work represents the first comprehensive exploration of the TST task as sentiment transfer across a diverse set of languages.

In this project note we describe our work to make better documentation for the Open Multilingual Wordnet (OMW), a platform integrating many open wordnets.This includes the documentation of the OMW website itself as well as of semantic relations used by the component wordnets.Some of this documentation work was done with the support of the Google Season of Docs.The OMW project page, which links both to the actual OMW server and the documentation has been moved to a new location: https://omwn.org.

This article describes the manual construction of a part of the Old English WordNet (Old-EWN) covering the semantic field of emotion terms.This manually constructed part of the wordnet is to be eventually integrated with the automatically generated/manually checked part covering the whole of the rest of the Old English lexicon (currently under construction).We present the workflow for the definition of these emotion synsets on the basis of a dataset produced by a specialist in this area.We also look at the enrichment of the original Global WordNet Association Lexical Markup Framework (GWA LMF) schema to include the extra information which this part of the OldEWN requires.In the final part of the article we discuss how the wordnet style of lexicon organisation can be used to share and disseminate research findings/datasets in lexical semantics.

Selecting an appropriate and relevant context forms an essential component for the efficacy of several information retrieval applications like Question Answering (QA) systems. The problem of Answer Sentence Selection (AS2) refers to the task of selecting sentences, from a larger text, that are relevant and contain the answer to users' queries. While there has been a lot of success in building AS2 systems trained on open-domain data (e.g., SQuAD, NQ), they do not generalize well in closed-domain settings, since domain adaptation can be challenging due to poor availability and annotation expense of domain-specific data. This paper proposes SEDAN, an effective self-learning framework to adapt AS2 models for domain-specific applications. We leverage large pre-trained language models to automatically generate domain-specific QA pairs for domain adaptation. We further fine-tune a pre-trained Sentence-BERT architecture to capture semantic relatedness between questions and answer sentences for AS2. Extensive experiments demonstrate the effectiveness of our proposed approach (over existing state-of-the-art AS2 baselines) on different Question Answering benchmark datasets.

In this paper we will discuss our preliminary work towards the construction of a WordNet for Old English, taking our inspiration from other similar WN construction projects for ancient languages such as Ancient Greek, Latin and Sanskrit. The Old English WordNet (OldEWN) will build upon this innovative work in a number of different ways which we articulate in the article, most importantly by treateating figurative meaning as a ‘first-class citizen’ in the structuring of the semantic system. From a more practical perspective we will describe our plan to utilize a pre-existing lexicographic resource and the naisc system to automatically compile a provisional version of the WordNet which will then be checked and enriched by Old English experts.

This paper introduces a new Magahi-Hindi-English (MHE) code-mixed data-set for similar language identification (SMLID), where Magahi is a less-resourced minority language. This corpus provides a language id at two levels: word and sentence. This data-set is the first Magahi-Hindi-English code-mixed data-set for similar language identification task. Furthermore, we will discuss the complexity of the data-set and provide a few baselines for the language identification task.

Language resources are a key component of natural language processing and related research and applications. Users of language resources have different needs in terms of format, language, topics, etc. for the data they need to use. Linghub (McCrae and Cimiano, 2015) was first developed for this purpose, using the capabilities of linked data to represent metadata, and tackling the heterogeneous metadata issue. Linghub aimed at helping language resources and technology users to easily find and retrieve relevant data, and identify important information on access, topics, etc. This work describes a rejuvenation and modernisation of the 2015 platform into using a popular open source data management system, DSpace, as foundation. The new platform, Linghub2, contains updated and extended resources, more languages offered, and continues the work towards homogenisation of metadata through conversions, through linkage to standardisation strategies and community groups, such as the Open Digital Rights Language (ODRL) community group.

Multilingual sentence embeddings capture rich semantic information not only for measuring similarity between texts but also for catering to a broad range of downstream crosslingual NLP tasks.State-of-the-art multilingual sentence embedding models require large parallel corpora to learn efficiently, which confines the scope of these models.In this paper, we propose a novel sentence embedding framework based on an unsupervised loss function for generating effective multilingual sentence embeddings, eliminating the need for parallel corpora.We capture semantic similarity and relatedness between sentences using a multitask loss function for training a dual encoder model mapping different languages onto the same vector space.We demonstrate the efficacy of an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks.The proposed unsupervised sentence embedding framework outperforms even supervised stateof-the-art methods for certain under-resourced languages on the Tatoeba dataset and on a monolingual benchmark.Further, we show enhanced zero-shot learning capabilities for more than 30 languages, with the model being trained on only 13 languages.Our model can be extended to a wide range of languages from any language family, as it overcomes the requirement of parallel corpora for training.

Automated Term Recognition (ATR) is the task of finding terminology from raw text. It involves designing and developing techniques for the mining of possible terms from the text and filtering these identified terms based on their scores calculated using scoring methodologies like frequency of occurrence and then ranking the terms. Current approaches often rely on statistics and regular expressions over part-of-speech tags to identify terms, but this is error-prone. We propose a deep learning technique to improve the process of identifying a possible sequence of terms. We improve the term recognition by using Bidirectional Encoder Representations from Transformers (BERT) based embeddings to identify which sequence of words is a term. This model is trained on Wikipedia titles. We assume all Wikipedia titles to be the positive set, and random n-grams generated from the raw text as a weak negative set. The positive and negative set will be trained using the Embed, Encode, Attend and Predict (EEAP) formulation using BERT as embeddings. The model will then be evaluated against different domain-specific corpora like GENIA - annotated biological terms and Krapivin - scientific papers from the computer science domain.

Knowledge graphs have been shown to be an important data structure for many applications, including chatbot development, data integration, and semantic search. In the enterprise domain, such graphs need to be constructed based on both structured (e.g. databases) and unstructured (e.g. textual) internal data sources; preferentially using automatic approaches due to the costs associated with manual construction of knowledge graphs. However, despite the growing body of research that leverages both structured and textual data sources in the context of automatic knowledge graph construction, the research community has centered on either one type of source or the other. In this paper, we conduct a preliminary literature review to investigate approaches that can be used for the integration of textual and structured data sources in the process of automatic knowledge graph construction. We highlight the solutions currently available for use within enterprises and point areas that would benefit from further research.

Words are defined based on their meanings in various ways in different resources.Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data.In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection.We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.

WordNet is the most widely used lexical resource for English, while Wikidata is one of the largest knowledge graphs of entity and concepts available.While, there is a clear difference in the focus of these two resources, there is also a significant overlap and as such a complete linking of these resources would have many uses.We propose the development of such a linking, first by means of the hapax legomenon links and secondly by the use of natural language processing techniques.We show that these can be done with high accuracy but that human validation is still necessary.This has resulted in over 9,000 links being added between these two resources.

The Global Wordnet Formats have been introduced to enable wordnets to have a common representation that can be integrated through the Global WordNet Grid.As a result of their adoption, a number of shortcomings of the format were identified, and in this paper we describe the extensions to the formats that address these issues.These include: ordering of senses, dependencies between wordnets, pronunciation, syntactic modelling, relations, sense keys, metadata and RDF support.Furthermore, we provide some perspectives on how these changes help in the integration of wordnets.

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines.Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor.In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts.The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages.We have performed our experiments on three shorttext datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets.Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings.

Conversational recommender systems focus on the task of suggesting products to users based on the conversation flow.Recently, the use of external knowledge in the form of knowledge graphs has shown to improve the performance in recommendation and dialogue systems.Information from knowledge graphs aids in enriching those systems by providing additional information such as closely related products and textual descriptions of the items.However, knowledge graphs are incomplete since they do not contain all factual information present on the web.Furthermore, when working on a specific domain, knowledge graphs in its entirety contribute towards extraneous information and noise.In this work, we study several subgraph construction methods and compare their performance across the recommendation task.We incorporate pre-trained embeddings from the subgraphs along with positional embeddings in our models.Extensive experiments show that our method has a relative improvement of at least 5.62% compared to the state-of-the-art on multiple metrics on the recommendation task.

Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbul-lying, and hate speech. Numerous works have been proposed to utilize these data for social and anti-social behavior analysis, document characterization, and sentiment analysis by predicting the contexts mostly for highly resourced languages like English. However, some languages are under-resources, e.g., South Asian languages like Bengali, Tamil, Assamese, Malayalam, that lack of computational resources for natural language processing. In this paper1, we provide several classification benchmarks for Bengali, an under-resourced language. We prepared three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis. We built the largest Bengali word embedding models to date based on 250 million articles, which we call BengFastText. We perform three experiments, covering document classification, sentiment analysis, and hate speech detection. We incorporate word embeddings into a Multichannel Convolutional-LSTM (MC-LSTM) network for predicting different types of hate speech, document classification, and sentiment analysis. Experiments demonstrate that BengFastText can capture the semantics of words from respective contexts correctly. Evaluations against several baseline embedding models, e.g., Word2Vec and GloVe yield up to 92.30%, 82.25%, and 90.45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

In this paper we discuss the experience of bringing together over 40 different wordnets. We introduce some extensions to the GWA wordnet LMF format proposed in Vossen et al. (2016) and look at how this new information can be displayed. Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalized synsets and lemmas, new parts of speech, and more. Many of these extensions already exist in multiple wordnets – the challenge was to find a compatible representation. To this end, we introduce a new version of the Open Multilingual Wordnet (Bond and Foster, 2013), that integrates a new set of tools that tests the extensions introduced by this new format, while also ensuring the integrity of the Collaborative Interlingual Index (CILI: Bond et al., 2016), avoiding the same new concept to be introduced through multiple projects.

In this paper we describe the contributions made by the European H2020 project “Prêt-à-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

Metaphor comprehension and understanding is a complex cognitive task that requires interpreting metaphors by grasping the interaction between the meaning of their target and source concepts. This is very challenging for humans, let alone computers. Thus, automatic metaphor interpretation is understudied in part due to the lack of publicly available datasets. The creation and manual annotation of such datasets is a demanding task which requires huge cognitive effort and time. Moreover, there will always be a question of accuracy and consistency of the annotated data due to the subjective nature of the problem. This work addresses these issues by presenting an annotation scheme to interpret verb-noun metaphoric expressions in text. The proposed approach is designed with the goal of reducing the workload on annotators and maintain consistency. Our methodology employs an automatic retrieval approach which utilises external lexical resources, word embeddings and semantic similarity to generate possible interpretations of identified metaphors in order to enable quick and accurate annotation. We validate our proposed approach by annotating around 1,500 metaphors in tweets which were annotated by six native English speakers. As a result of this work, we publish as linked data the first gold standard dataset for metaphor interpretation which will facilitate research in this area.

Under-resourced languages are a significant challenge for statistical approaches to machine translation, and recently it has been shown that the usage of training data from closely-related languages can improve machine translation quality of these languages. While languages within the same language family share many properties, many under-resourced languages are written in their own native script, which makes taking advantage of these language similarities difficult. In this paper, we propose to alleviate the problem of different scripts by transcribing the native script into common representation i.e. the Latin script or the International Phonetic Alphabet (IPA). In particular, we compare the difference between coarse-grained transliteration to the Latin script and fine-grained IPA transliteration. We performed experiments on the language pairs English-Tamil, English-Telugu, and English-Kannada translation task. Our results show improvements in terms of the BLEU, METEOR and chrF scores from transliteration and we find that the transliteration into the Latin script outperforms the fine-grained IPA transcription.

Metaphor is one of the most important elements of human communication, especially in informal settings such as social media. There have been a number of datasets created for metaphor identification, however, this task has proven difficult due to the nebulous nature of metaphoricity. In this paper, we present a crowd-sourcing approach for the creation of a dataset for metaphor identification, that is able to rapidly achieve large coverage over the different usages of metaphor in a given corpus while maintaining high accuracy. We validate this methodology by creating a set of 2,500 manually annotated tweets in English, for which we achieve inter-annotator agreement scores over 0.8, which is higher than other reported results that did not limit the task. This methodology is based on the use of an existing classifier for metaphor in order to assist in the identification and the selection of the examples for annotation, in a way that reduces the cognitive load for annotators and enables quick and accurate annotation. We selected a corpus of both general language tweets and political tweets relating to Brexit and we compare the resulting corpus on these two domains. As a result of this work, we have published the first dataset of tweets annotated for metaphors, which we believe will be invaluable for the development, training and evaluation of approaches for metaphor identification in tweets.

We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model.In particular, this version of WordNet, which we call English WordNet 2019, which has been developed by multiple people around the world through GitHub, fixes many errors in previous wordnets for English.We give some details of the changes that have been made in this version and give some perspectives about likely future changes that will be made as this project continues to evolve.

Wordnets are extensively used in natural language processing, but the current approaches for manually building a wordnet from scratch involves large research groups for a long period of time, which are typically not available for under-resourced languages.Even if wordnet-like resources are available for under-resourced languages, they are often not easily accessible, which can alter the results of applications using these resources.Our proposed method presents an expand approach for improving and generating wordnets with the help of machine translation.We apply our methods to improve and extend wordnets for the Dravidian languages, i.e., Tamil, Telugu, Kannada, which are severly under-resourced languages.We report evaluation results of the generated wordnet senses in term of precision for these languages.In addition to that, we carried out a manual evaluation of the translations for the Tamil language, where we demonstrate that our approach can aid in improving wordnet resources for under-resourced Dravidian languages.

Lexical resource differ from encyclopaedic resources and represent two distinct types of resource covering general language and named entities respectively.However, many lexical resources, including Princeton WordNet, contain many proper nouns, referring to named entities in the world yet it is not possible or desirable for a lexical resource to cover all named entities that may reasonably occur in a text.In this paper, we propose that instead of including synsets for instance concepts PWN should instead provide links to Wikipedia articles describing the concept.In order to enable this we have created a gold-quality mapping between all of the 7,742 instances in PWN and Wikipedia (where such a mapping is possible).As such, this resource aims to provide a gold standard for link discovery, while also allowing PWN to distinguish itself from other resources such as DBpedia or BabelNet.Moreover, this linking connects PWN to the Linguistic Linked Open Data cloud, thus creating a richer, more usable resource for natural language processing.

Princeton WordNet is one of the most widely-used resources for natural language processing, but is updated only infrequently and cannot keep up with the fast-changing usage of the English language on social media platforms such as Twitter.The Colloquial WordNet aims to provide an open platform whereby anyone can contribute, while still following the structure of WordNet.Many crowdsourced lexical resources often have significant quality issues, and as such care must be taken in the design of the interface to ensure quality.In this paper, we present the development of a platform that can be opened on the Web to any lexicographer who wishes to contribute to this resource and the lexicographic methodology applied by this interface.

The paper describes objectives, concept and methodology for ELEXIS, a European infrastructure fostering cooperation and information exchange among lexicographical research communities.The infrastructure is a newly granted project under the Horizon 2020 INFRAIA call, with the topic Integrating Activities for Starting Communities.The project is planned to start in January 2018.

Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user-generated media content in automated systems such as call-centre operations, recommendations, and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time-consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: 1) for text processing: emotion and sentiment recognition; 2) for audio processing: emotion, age, and gender recognition; 3) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation; and 4) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this paper, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use cases show its effectiveness, namely, emotion-driven smart TV, call center monitoring, and brand reputation analysis.

Understanding the differences underlying the scope, usage and content of language data requires the provision of a clarifying terminological basis which is integrated in the metadata describing a particular language resource. While terminological resources such as the SIL Glossary of Linguistic Terms, ISOcat or the GOLD ontology provide a considerable amount of linguistic terms, their practical usage is limited to a look up of a defined term whose relation to other terms is unspecified or insufficient. Therefore, in this paper we propose an ontology for linguistic terminology, called OnLiT. It is a data model which can be used to represent linguistic terms and concepts in a semantically interrelated data structure and, thus, overcomes prevalent isolating definition-based term descriptions. OnLiT is based on the LiDo Glossary of Linguistic Terms and enables the creation of RDF datasets, that represent linguistic terms and their meanings within the whole or a subdomain of linguistics.

Princeton WordNet is one of the most important resources for natural language processing, but is only available for English. While it has been translated using the expand approach to many other languages, this is an expensive manual process. Therefore it would be beneficial to have a high-quality automatic translation approach that would support NLP techniques, which rely on WordNet in new languages. The translation of wordnets is fundamentally complex because of the need to translate all senses of a word including low frequency senses, which is very challenging for current machine translation approaches. For this reason we leverage existing translations of WordNet in other languages to identify contextual information for wordnet senses from a large set of generic parallel corpora. We evaluate our approach using 10 translated wordnets for European languages. Our experiment shows a significant improvement over translation without any contextual information. Furthermore, we evaluate how the choice of pivot languages affects performance of multilingual word sense disambiguation.

This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI).It is designed to make possible coordination between multiple loosely coupled wordnet projects.The structure of the CILI is based on the Interlingual index first proposed in the Eu-roWordNet project with several pragmatic extensions: an explicit open license, definitions in English and links to wordnets in the Global Wordnet Grid.

In this paper, we describe a new and improved Global Wordnet Grid that takes advantage of the Collaborative InterLingual Index (CILI).Currently, the Open Multilingal Wordnet has made many wordnets accessible as a single linked wordnet, but as it used the Princeton Wordnet of English (PWN) as a pivot, it loses concepts that are not part of PWN.The technical solution to this, a central registry of concepts, as proposed in the EuroWordnet project through the InterLingual Index, has been known for many years.However, the practical issues of how to host this index and who decides what goes in remained unsolved.Inspired by current practice in the Semantic Web and the Linked Open Data community, we propose a way to solve this issue.In this paper we define the principles and protocols for contributing to the Grid.We tested them on two use cases, adding version 3.1 of the Princeton WordNet to a CILI based on 3.0 and adding the Open Dutch Wordnet, to validate the current set up.This paper aims to be a call for action that we hope will be further discussed and ultimately taken up by the whole wordnet community.

Cross-lingual topic modelling has applications in machine translation, word sense disambiguation and terminology alignment.Multilingual extensions of approaches based on latent (LSI), generative (LDA, PLSI) as well as explicit (ESA) topic modelling can induce an interlingual topic space allowing documents in different languages to be mapped into the same space and thus to be compared across languages.In this paper, we present a novel approach that combines latent and explicit topic modelling approaches in the sense that it builds on a set of explicitly defined topics, but then computes latent relations between these.Thus, the method combines the benefits of both explicit and latent topic modelling approaches.We show that on a crosslingual mate retrieval task, our model significantly outperforms LDA, LSI, and ESA, as well as a baseline that translates every word in a document into the target language.

BACKGROUND: Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst 1, but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. RESULTS: We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. CONCLUSION: We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.