Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text?We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "这/那" in Chinese, as a novel probe for grounded knowledge.Using 6,400 responses from 320 native speakers, we establish a human baseline: English speakers reliably distinguish proximal-distal referents but struggle with perspective-taking, while Chinese speakers switch perspectives fluently but tolerate distal ambiguity.In contrast, five state-ofthe-art LLMs fail to inherently understand the proximal-distal contrast and show no cultural differences, defaulting to English-centric reasoning.Our study contributes (i) a new task, based on demonstratives, as a new lens for evaluating embodied cognition and cultural conventions; (ii) empirical evidence of cross-cultural asymmetries in human interpretation; (iii) a new perspective on the egocentric-sociocentric debate, showing both orientations coexist but vary across languages; and (iv) a call to address individual variation in future model design.Model Gpt5.

Utilizing pre-trained generative models for sentiment element extraction has recently significantly enhanced aspect-based sentiment analysis benchmarks. Nonetheless, these models have two significant drawbacks: 1) high-computational cost in both the inference time and hardware requirement. 2) Lack of explicit modeling as they model the connections between sentiment elements with fragile natural or notational language target sequence. To overcome these challenges, we present a novel opinion tree parsing model designed to swiftly parse sentiment elements from an opinion tree. This approach not only accelerates the process but also explicitly unveils a more comprehensive and fully articulated aspect-level sentiment structure. Our method begins by introducing a pioneering context-free opinion grammar to standardize the opinion tree structure. Subsequently, we leverage a neural chart-based opinion tree parser to thoroughly explore the interconnections among sentiment elements and parse them into a structured opinion tree. Extensive experiments underscore the effectiveness of our proposed model and the capability of the opinion tree parser, particularly when coupled with the introduced context-free opinion grammar. Crucially, the results confirm the superior speed of our model compared to the SOTA baselines.

The research on classical Chinese event extraction trends to directly graft the complex modeling from English or modern Chinese works, neglecting the utilization of the unique characteristic of this language. We argue that, compared with grafting the sophisticated methods from other languages, focusing on classical Chinese’s inimitable source of Ancient Literature could provide us with extra and comprehensive semantics in event extraction. Motivated by this, we propose a Literary Vision-Language Model (VLM) for classical Chinese event extraction, integrating with literature annotations, historical background and character glyph to capture the inner- and outer-context information from the sequence. Extensive experiments build a new state-of-the-art performance in the GuwenEE, CHED datasets, which underscores the effectiveness of our proposed VLM, and more importantly, these unique features can be obtained precisely at nearly zero cost.

With thousand years of history, calligraphy serve as one of the representative symbols of Chinese culture. Increasing works try to digitize calligraphy by recognizing the context of calligraphy for better preservation and propagation. However, previous works stick to isolated single character recognition, not only requires unpractical manual splitting into characters, but also abandon the enriched context information that could be supplementary. To this end, we construct the pioneering end-to-end calligraphy recognition benchmark dataset, this dataset is challenging due to both the visual variations such as different writing styles and the textual understanding such as the domain shift in semantics. We further propose CalligraphicOCR (COCR) equipped with calligraphic image augmentation and action-based corrector targeted at the challenging root of this setting. Experiments demonstrate the advantage of our proposed model over cutting-edge baselines, underscoring the necessity of introducing this new setting, thereby facilitating a solid precondition for protecting and propagating the already scarce resources.

Traditional Ecological Knowledge (TEK) has been recognized as a shared cultural heritage and a crucial instrument to tackle today’s environmental challenges. In this paper, we deal with historical ecological knowledge, a special type of TEK that is based on ancient language texts. In particular, we aim to build a language resource based on Shanhai Jing (The Classic of Mountains and Seas). Written 2000 years ago, Shanhai Jing is a record of flora and fauna in ancient China, anchored by mountains (shan) and seas (hai). This study focuses on the entities in the Shan Jing part and builds a knowledge base for them. We adopt a pattern-driven and bottom-up strategy to accommodate two features of the source: highly stylized narrative and juxtaposition of knowledge from multiple domains. The PRF values of both entity and relationship extraction are above 96%. Quality assurance measures like entity disambiguation and resolution were done by domain experts. Neo4j graph database is used to visualize the result. We think the knowledge base, containing 1432 systematically classified entities and 3294 relationships, can provide the foundation for the construction of a historical ecological knowledge base of China. Additionally, the ruled-based text-matching method can be helpful in ancient language processing.

For a conversation to help and support, speakers should maintain an "effect-effort" trade-off.As outlined in the gist of "Cognitive Relevance Principle", helpful speakers should optimize the "cognitive relevance" through maximizing the "cognitive effects" and minimizing the "processing effort" imposed on listeners.Although preference learning methods provide a boon for studies concerning "effect-optimization", none have delved into "effort-optimization" which is pivotal to the acquisition of "optimal relevance" for emotional support conversation agents.To address this gap, we integrate the "Cognitive Relevance Principle" into emotional support agents in the environment of multi-turn conversation.The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness, and supportiveness.This study offers compelling evidence for the effectiveness of the "Relevance Principle" in generating human-like, helpful, and harmless emotional support conversations.

Financial sentiment analysis plays a pivotal role in understanding market dynamics and investor sentiment. In this paper, we propose the Supervised Cross-Momentum Contrast (SuCroMoCo) framework, a novel approach for financial sentiment analysis. SuCroMoCo leverages supervised contrastive learning and cross-momentum contrast to align financial text representations with prototypical representations based on sentiment categories. This alignment greatly improves classification performance, addressing the limitations of pre-trained language models (PLMs) in fully grasping the intricate nature of financial text. Through extensive experiments, we demonstrate that SuCroMoCo outperforms existing PLMs-based approaches and Large Language Models (LLMs) on diverse benchmark datasets. The code and datasets can be found in https://github.com/PengBO-O/SuCroMoCo.

Abstract Understanding the nature of meaning and its extensions (with metaphor as one typical kind) has been one core issue in figurative language study since Aristotle’s time. This research takes a computational cognitive perspective to model metaphor based on the assumption that meaning is perceptual, embodied, and encyclopedic. We model word meaning representation for metaphor detection with embodiment information obtained from behavioral experiments. Our work is the first attempt to incorporate sensorimotor knowledge into neural networks for metaphor detection, and demonstrates superiority, consistency, and interpretability compared to peer systems based on two general datasets. In addition, with cross-sectional analysis of different feature schemas, our results suggest that metaphor, as a device of cognitive conceptualization, can be ‘learned’ from the perceptual and actional information independent of several more explicit levels of linguistic representation. The access to such knowledge allows us to probe further into word meaning mapping tendencies relevant to our conceptualization and reaction to the physical world.

The proliferation of COVID-19 fake news on social media poses a severe threat to the health information ecosystem. We show that affective computing can make significant contributions to combat this infodemic. Given that fake news is often presented with emotional appeals, we propose a new perspective on the role of emotion in the attitudes, perceptions, and behaviors of the dissemination of information. We study emotions in conjunction with fake news, and explore different aspects of their interaction. To process both emotion and ‘falsehood’ based on the same set of data, we auto-tag emotions on existing COVID-19 fake news datasets following an established emotion taxonomy. More specifically, based on the distribution of seven basic emotions (e.g.Happiness, Like, Fear, Sadness, Surprise, Disgust, Anger), we find across domains and styles that COVID-19 fake news is dominated by emotions ofFear(e.g., of coronavirus), andDisgust(e.g., of social conflicts). In addition, the framing of fake news in terms of gain-versus-loss reveals a close correlation between emotions, perceptions, and collective human reactions. Our analysis confirms the significant role of emotionFearin the spreading of the fake news, especially when contextualized in the loss frame. Our study points to a future direction of incorporating emotion footprints in models of automatic fake news detection, and establishes an affective computing approach to information quality in general and fake news detection in particular.

BACKGROUND: The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain. RESULTS: In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing. CONCLUSION: The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.

We present Scikit-talk, an open-source toolkit for processing collections of real-world conversational speech in Python.First of its kind, the toolkit equips those interested in studying or modeling conversations with an easyto-use interface to build and explore large collections of transcriptions and annotations of talk-in-interaction.Designed for applications in speech processing and Conversational AI, Scikit-talk provides tools to custombuild datasets for tasks such as intent prototyping, dialog flow testing, and conversation design.Its preprocessor module comes with several pre-built interfaces for common transcription formats, which aim to make working across multiple data sources more accessible.The explorer module provides a collection of tools to explore and analyse this data type via string matching and unsupervised machine learning techniques.Scikit-talk serves as a platform to collect and connect different transcription formats and representations of talk, enabling the user to quickly build multilingual datasets of varying detail and granularity.Thus, the toolkit aims to make working with authentic conversational speech data in Python more accessible and to provide the user with comprehensive options to work with representations of talk in appropriate detail for any downstream task.For the latest updates and information on currently supported languages and language resources, please refer to

Word embeddings are vectorial semantic representations built with either counting or predicting techniques aimed at capturing shades of meaning from word co-occurrences. Since their introduction, these representations have been criticized for lacking interpretable dimensions. This property of word embeddings limits our understanding of the semantic features they actually encode. Moreover, it contributes to the “black box” nature of the tasks in which they are used, since the reasons for word embedding performance often remain opaque to humans. In this contribution, we explore the semantic properties encoded in word embeddings by mapping them onto interpretable vectors, consisting of explicit and neurobiologically motivated semantic features (Binder et al. 2016). Our exploration takes into account different types of embeddings, including factorized count vectors and predict models (Skip-Gram, GloVe, etc.), as well as the most recent contextualized representations (i.e., ELMo and BERT). In our analysis, we first evaluate the quality of the mapping in a retrieval task, then we shed light on the semantic features that are better encoded in each embedding type. A large number of probing tasks is finally set to assess how the original and the mapped embeddings perform in discriminating semantic categories. For each probing task, we identify the most relevant semantic features and we show that there is a correlation between the embedding performance and how they encode those features. This study sets itself as a step forward in understanding which aspects of meaning are captured by vector spaces, by proposing a new and simple method to carve human-interpretable semantic representations from distributional vectors.

Abstract Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic‐based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution‐based data augmentation methods, one of which is thesaurus‐based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST‐2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM‐AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well‐designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms.

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks.

While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.

Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.

Abstract In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment-final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.

Abstract This paper proposes a robust text classification and correspondence analysis approach to identification of similar languages. In particular, we propose to use the readily available information of clauses and word length distribution to model similar languages. The modeling and classification are based on the hypothesis that languages are self-adaptive complex systems and hence can be classified by dynamic features describing the system, especially in terms of distributional relations of constituents of a system. For similar languages whose grammatical differences are often subtle, classification based on dynamic system features should be more effective. To test this hypothesis, we considered both regional and genre varieties of Mandarin Chinese for classification. The data are extracted from two comparable balanced corpora to minimize possible confounding factors. The two corpora are the Sinica Corpus from Taiwan and the Lancaster Corpus of Mandarin Chinese from Mainland China, and the two genres are reportage and review. Our text classification and correspondence analysis results show that the linguistically felicitous two-level constituency model combining power functions between word and clauses effectively classifies the two varieties of Chinese for both genres. In addition, we found that genres do have compounding effect on classification of regional varieties. In particular, reportage in two varieties is more likely to be classified than review, corroborating the complex system view of language variations. That is, language variations and changes typically do not take place evenly across the board for the complete language system. This further enhances our hypothesis that dynamic complex system features, such as the power functions captured by the Menzerath–Altmann law, provide effective models in classifications of similar languages.

Abstract Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modelled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension.We evaluate SDMon two recently introduced compositionality data sets, and our results show that combining a simple compositionalmodel with event knowledge constantly improves performances, even with dif ferent types of word embeddings.

202101 bcrc

Attention models are proposed in sentiment analysis because some words are more important than others.However, most existing methods either use local context based text information or user preference information.In this work, we propose a novel attention model trained by cognition grounded eye-tracking data.A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data.The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis.As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents.Different attention mechanisms can also be incorporated to capture other aspects of attentions.Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly.This brings insight to how cognition grounded data can be brought into NLP tasks.

202303 bcww

In this paper, we claim that vector cosine – which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models – can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. To prove it, we describe and evaluate APSyn, a variant of the Average Precision that, without any optimization, outperforms the vector cosine and the co-occurrence on the standard ESL test set, with an improvement ranging between +9.00% and +17.98%, depending on the number of chosen top contexts.

In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT13 achieves an F1 score of 88.3%, against a baseline of 57.6% (vector cosine). When the classification is binary, ROOT13 achieves the following results: hypernyms-co-hyponyms (93.4% vs. 60.2%), hypernyms-random (92.3% vs. 65.5%) and co-hyponyms-random (97.3% vs. 81.5%). Our results are competitive with state-of-the-art models.

Inferring latent user preferences using both structured and unstructured data is an important social computing task. In this paper, we propose a user preference representation based on user activities embedded in unstructured data to better encode the homophily theory. The representation of an individual user is learned using a embedding based method to integrate latent user preferences in social media. The method has the ability to integrate a variety of user activities based cues from user comments, user social network (i.e; follower/followee connections) and user interested topics which are indicated by the topics a user has participated in. Experiments are conducted to evaluate the prediction of each user's favorite team as a part of user preferences in a dataset collected from the Hu-pu basketball discussion forum.1Results clearly indicate that our proposed user representation outperforms other user representation baselines. Integrating user social network and user interested topics with user comments can improve the overall performance of user preference prediction.

Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words.Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations.In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies.We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better.Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.

Sentiment and emotion classification have been popularly but separately studied in natural language processing. In this paper, we address joint learning on sentiment and emotion classification where both the labeled data for sentiment and emotion classification are available. The objective of this joint-learning is to benefit the two tasks from each other for improving their performances. Specifically, an extra data set that is annotated with both sentiment and emotion labels are employed to estimate the transformation probability between the two kinds of labels. Furthermore, the transformation probability is leveraged to transfer the classification labels to benefit the two tasks from each other. Empirical studies demonstrate the effectiveness of our approach for the novel joint learning task.

Most theories of emotion treat recognition of a triggering cause event as an integral part of emotion processing. This paper proposes emotion cause detection as a new research area in emotion processing. As a first step toward fully automatic inference of emotion‐cause correlation, we propose a text‐driven, rule‐based approach to emotion cause detection in Chinese. First, we constructed a Chinese emotion cause annotated corpus based on our proposed annotation scheme. Next, we analyzed the corpus data, which yielded the identification of seven groups of linguistic cues and two sets of generalized linguistic rules for the detection of emotion causes. We then developed a rule‐based system for emotion cause detection based on the linguistic rules. In addition, we proposed an evaluation scheme with two phases for performance assessment. The results of our experiments show that our system achieved a promising performance for cause occurrence detection, as well as for cause event detection. The current study should lay the groundwork for future research on the inferences of implicit information and the discovery of new information based on cause‐event relation.

In this paper, we propose a lexicon-driven and ontologymerging methodology of constructing diachronic domain knowledge via Sinica BOW, a bilingual ontological lexical resource based on WordNet and SUMO ontology. The main domain knowledge that we model our specialized ontology on is GuangQunFangPu, a Chinese classic literature of botany. Our studies yields promising result, we believe that the proposed research scenario will boost the on-going development of e-Humanities.