Results 11 to 20 of about 15,951 (231)
A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model [PDF]
PeerJ Computer Science, 2023 Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection.
Miada Almasri +2 more
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Keyphrase extraction by the use of glove and ResNeXt optimized by enhanced human evolutionary optimization (EHEO) algorithm [PDF]
Scientific ReportsKeyphrase extraction (KPE) is an essential process in natural language processing, facilitating the document content summarization for diverse uses like search engine optimization and information retrieval.
Chao Pan, Yanshu Liu, Mohammad Sarabi
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Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 The SemEval-2007 task to disambiguate prepositions was designed as a lexical sample task. A set of over 25,000 instances was developed, covering 34 of the most frequent English prepositions, with two-thirds of the instances for training and one-third as the test set.
Ken Litkowski, Orin Hargraves
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SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), 2023 We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan ...
Shamsuddeen Hassan Muhammad +9 more
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Multilingual Fine-Grained Named Entity Recognition [PDF]
Computer Science Journal of Moldova, 2023 The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs,
Viorica-Camelia Lupancu, Adrian Iftene
doaj +1 more source
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 We provide an overview of the metonymy resolution shared task organised within SemEval-2007. We describe the problem, the data provided to participants, and the evaluation measures we used to assess performance. We also give an overview of the systems that have taken part in the task, and discuss possible directions for future work.
K. Markert, NISSIM, MALVINA
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A metaheuristic with a neural surrogate function for Word Sense Disambiguation
Machine Learning with Applications, 2022 Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context.
Azim Keshavarzian Nodehi +1 more
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SemEval-2015 Task 8: SpaceEval [PDF]
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015 Human languages exhibit a variety of strategies for communicating spatial information, including toponyms, spatial nominals, locations that are described in relation to other locations, and movements along paths. SpaceEval is a combined information extraction and classification task with the goal of identifying and categorizing such spatial information.
Pustejovsky, James +5 more
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What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP [PDF]
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, 2021 SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval.
Wysocki, Oskar +3 more
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Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, we describe the data set used in the evaluation and the results obtained by the participating systems.
Strapparava C, Mihalcea R
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