#SEMEVAL 2015 TASK3 FULL#
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#SEMEVAL 2015 TASK3 PC#
PC member / reviewer in conferences (* indicates AC or SPC):Ģ016: CIKM, CSCW, ECIR, EMNLP, ICWSM, HCMOP, SIGIR.Ģ018: CIKM, CSCW, ECIR, EMNLP, HICSS51, ICNLSP, ICWSM, SDCS, AAAI, ACL*, CHI, CIKM, ECIR, HT, ICWSM, LREC, SocInfo*, WebSci. Reviewer in journals: ACM Transactions on Information Systems (TOIS), ACM Transactions on Internet Technology (TOIT), Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Information Forensics and Security (TIFS), Elsevier IP&M, Elsevier Information Systems, Elsevier Expert Systems, Elsevier Telecommunication and Informatics, Elsevier World Patent Information, Elsevier Computer Speech & Language, Elsevier Journal of Network and Computer Applications, Springer IR Journal, AI Communications, Computational Linguistics, WWW Journal, Applied Computing & Informatics, Social Network Analysis and Mining (SNAM), Online Information Review Journal, Journal of Telemedicine and Telecare. Detection and Characterization of Stance on Social Media, ICWSM, Jun 2020.
Possible Directions for Building a Career in Data Science, Alexandria University, Egypt, Dec 2018. Possible Directions for Building a Career in Data Science, Cairo University, Egypt, Dec 2017. WANLP 2021: The Sixth Arabic Natural Language Processing Workshop, 2021 4th Workshop on Arabic Corpora and Processing Tools, 2020 WANLP 2019: The Fourth Arabic Natural Language Processing Workshop, 2019 BroDyn: Analysis of Broad Dynamic Topics over Social Media, 2018 3rd Workshop on Arabic Corpora and Processing Tools, 2018 2nd Workshop on Arabic Corpora and Processing Tools, 2016 SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic. WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection In Arabic. those that can be potentially useful to the user (e.g., because they can help educate him/her on the subject) vs. First, subtask A asks for identifying the posts in the answer thread that answer the question well vs. SemEval-2021 Task 7: Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks. In our SemEval-2015 Task 3, we proposed two subtasks. OSACT4-2020 Shared Task: Arabic offensive language detection. SemEval-2016 Task 3: Community Question Answering. SemEval-2015 Task 3: Answer Selection in Community Question Answering. Social Network Analysis and Mining ( SNAM).Ĭo-organizer of the following shared tasks: Information filtering, social search, patent search, evaluation metrics, Arabic IR, and cross-language IR (CLIR) Text mining, classification, sentiment analysis, sarcasm detection. Further experiments show that our model is general and can be applied to other sentence-pair modeling tasks. Experimental results on SemEval-2015 Task 3 dataset demonstrate that our proposed method substantially outperforms several strong baselines. Social content analysis, political bias detection, and users' behavior analysis/prediction Finally based on the aggregated evidence, the decision is made through a fully connected network. I am the founder and director of the Social Media Analysis and Support for Humanity ( SMASH) group at the University of Edinburgh. I have large industrial background from working earlier for Qatar Computing Research Institute ( QCRI), Microsoft, and IBM. School of Computing, Dublin City University ( DCU). My main expertise is in computational social science, data mining, and natural language processing. I am also a faculty fellow at The Alan Turing Institute. These scores are better than the baselines, especially for subtasks A–C.I am a faculty member at the Institute for Language, Cognition and Computation ( ILCC), part of the School of Informatics, the Univeristy of Edinburgh. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. tains paraphrases that are lexically diverse and (iii). A variety of approaches and features were used by the participating systems to address the different subtasks. SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT). Unfortunately, no teams participated in subtask E. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A–D. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. This year, we reran the four subtasks from SemEval-2016: (A) Question–Comment Similarity, (B) Question–Question Similarity, (C) Question– External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 20 for training, and fresh data for testing.
We describe SemEval2017 Task 3 on Community Question Answering.
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