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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: JMIR Form Res (2022)
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Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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In: ISSN: 1866-9956 ; EISSN: 1866-9964 ; Cognitive Computation ; https://hal.archives-ouvertes.fr/hal-03275549 ; Cognitive Computation, Springer, 2021, 13 (4), ⟨10.1007/s12559-021-09862-5⟩ ; https://link.springer.com/article/10.1007%2Fs12559-021-09862-5 (2021)
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| 4 |
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 ; Conference on Findings of the Association for Computational Linguistics (EMNLP 2021) ; https://hal.archives-ouvertes.fr/hal-03468351 ; Conference on Findings of the Association for Computational Linguistics (EMNLP 2021), ACL: Association for Computational Linguistics, Nov 2021, Punta Cana, Dominican Republic. pp.2833-2844 ; https://aclanthology.org/2021.findings-emnlp.242/ (2021)
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| 5 |
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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In: Cognit Comput (2021)
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Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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Irony Detection in a Multilingual Context
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In: ECIR ; https://hal.archives-ouvertes.fr/hal-02889008 ; ECIR, Apr 2020, online, Portugal (2020)
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| 8 |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ; 58th Annual Meeting of the Association for Computational Linguistics 2020 ; https://jeannicod.ccsd.cnrs.fr/ijn_03046501 ; 58th Annual Meeting of the Association for Computational Linguistics 2020, ACL: Association for Computational Linguistics, Jul 2020, Online, France. pp.4055-4066, ⟨10.18653/v1/2020.acl-main.373⟩ ; https://www.aclweb.org/anthology/2020.acl-main.373/ (2020)
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| 9 |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03046097 ; Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Online, United States. pp.4055-4066, ⟨10.18653/v1/2020.acl-main.373⟩ ; https://acl2020.org/ (2020)
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| 12 |
Multilingual and Multitarget Hate Speech Detection in Tweets
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In: Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019) ; https://hal.archives-ouvertes.fr/hal-02567777 ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019), Jul 2019, Toulouse, France. pp.351-360 ; https://www.aclweb.org/anthology/2019.jeptalnrecital-court.21/ (2019)
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| 13 |
IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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| 14 |
Can we Predict Locations in Tweets? A Machine Learning Approach
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In: ISSN: 0976-0962 ; International Journal of Computational Linguistics and Applications ; https://hal.archives-ouvertes.fr/hal-02901421 ; International Journal of Computational Linguistics and Applications, Alexander Gelbukh, 2018, 9, pp.0 (2018)
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| 15 |
IRIT at e-Risk 2018
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In: CLEF 2018 Working Notes ; 9th Conference and Labs of the Evaluation Forum, Living Labs (CLEF 2018) ; https://hal.archives-ouvertes.fr/hal-02290007 ; 9th Conference and Labs of the Evaluation Forum, Living Labs (CLEF 2018), Sep 2018, Avignon, France. pp.1-12 (2018)
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| 16 |
IRIT at e-Risk
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In: CLEF 2017 - CLEF 2017 Working Notes ; International Conference of the CLEF Association, CLEF 2017 Labs Working Notes (CLEF 2017) ; https://hal.archives-ouvertes.fr/hal-01912779 ; International Conference of the CLEF Association, CLEF 2017 Labs Working Notes (CLEF 2017), Sep 2017, Dublin, Ireland. pp. 1-7 (2017)
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| 17 |
Predicting Locations in Tweets
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In: CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02624131 ; CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2017, Budapest, Hungary (2017)
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| 18 |
Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study
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In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1 ; 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686475 ; 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.262 - 272 (2017)
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| 19 |
SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media
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In: 3rd International Conference on Arabic Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686504 ; 3rd International Conference on Arabic Computational Linguistics, Nov 2017, Dubaï, United Arab Emirates. pp.161 - 168, ⟨10.1016/j.procs.2017.10.105⟩ (2017)
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| 20 |
INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned
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In: ISSN: 0306-4573 ; Information Processing and Management ; https://hal-amu.archives-ouvertes.fr/hal-01479297 ; Information Processing and Management, Elsevier, 2016, 52 (5), pp.801-819. ⟨10.1016/j.ipm.2016.03.002⟩ (2016)
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Abstract:
International audience ; Microblogging platforms such as Twitter are increasingly used for on-line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words). This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary. Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part-of-speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering. This paper provides a full summary report on the four-year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task.
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Keyword:
[INFO]Computer Science [cs]; automatic summarization; contextual information retrieval; focus information retrieval; Kullback-Leibler divergence; natural language processing; question answering; Short text contextualization; text informativeness; text readability; textual references; tweet contextualization; tweet understanding; Wikipedia
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URL: https://hal-amu.archives-ouvertes.fr/hal-01479297/file/bellot_16939.pdf
https://doi.org/10.1016/j.ipm.2016.03.002
https://hal-amu.archives-ouvertes.fr/hal-01479297/document
https://hal-amu.archives-ouvertes.fr/hal-01479297
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