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Social changes through the lens of language: A big data study of Chinese modal verbs
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In: PLoS One (2022)
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Database of word-level statistics for Mandarin Chinese (DoWLS-MAN)
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-03328510 ; Behavior Research Methods, Psychonomic Society, Inc, In press, ⟨10.3758/s13428-021-01620-7⟩ (2021)
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Not all arguments are processed equally: a distributional model of argument complexity
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In: ISSN: 1574-020X ; EISSN: 1574-0218 ; Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-03533181 ; Language Resources and Evaluation, Springer Verlag, 2021, 55 (4), pp.873-900. ⟨10.1007/s10579-021-09533-9⟩ (2021)
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Distribution of Mandarin synesthetic adjectives in five senses
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Not all arguments are processed equally: a distributional model of argument complexity
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In: Springer Netherlands (2021)
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From Contact Prevention to Social Distancing: The Co-Evolution of Bilingual Neologisms and Public Health Campaigns in Two Cities in the Time of COVID-19 ...
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From Contact Prevention to Social Distancing: The Co-Evolution of Bilingual Neologisms and Public Health Campaigns in Two Cities in the Time of COVID-19 ...
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Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques ...
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Decoding Word Embeddings with Brain-Based Semantic Features ...
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Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT ...
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Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features ...
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Improving attention model based on cognition grounded data for sentiment analysis
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Abstract:
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.
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URL: https://doi.org/10.1109/TAFFC.2019.2903056
http://repository.essex.ac.uk/27749/1/Improving%20attention.pdf
http://repository.essex.ac.uk/27749/
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Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation
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In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL-IJCNLP) ; https://hal.archives-ouvertes.fr/hal-03042410 ; Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL-IJCNLP), Dec 2020, Suzhou, China (2020)
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