Results 31 to 40 of about 21,648 (265)
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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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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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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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-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) [PDF]
International Workshop on Semantic Evaluation, 2020 We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020).
Marcos Zampieri +8 more
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Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 This paper presents the task definition, resources, and the single participant system for Task 12: Turkish Lexical Sample Task (TLST), which was organized in the SemEval-2007 evaluation exercise. The methodology followed for developing the specific linguistic resources necessary for the task has been described in this context.
Zeynep Orhan +2 more
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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) [PDF]
International Workshop on Semantic Evaluation, 2019 We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains
Marcos Zampieri +5 more
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Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free to use any lexical resource.
D. MCCARTHY, NAVIGLI, ROBERTO
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KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media [PDF]
International Workshop on Semantic Evaluation, 2020 In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show
Ali Safaya +2 more
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SemEval-2021 Task 12: Learning with Disagreements [PDF]
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), 2021 Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization.
Uma, Alexandra +7 more
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