Results 41 to 50 of about 15,278 (236)
BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense [PDF]
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020 This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 - Commonsense Validation and Explanation. We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation.
Pavel Smrz +3 more
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COVID‐19 clinical medical relationship extraction based on MPNet
IET Cyber-Physical Systems: Theory &Applications, Volume 8, Issue 2, Page 119-129, June 2023., 2023 This paper proposes a deep learning method based on a COVID‐19 clinical trial data relation extraction model. The model adopts MPNet model, bidirectional‐GRU network, MAtt mechanism and CRF reasoning layer integrated architecture to improve the problem that static word vectors cannot represent ambiguity through pre‐training language models.
Su Qianmin +4 more
wiley +1 more source
SemEval-2018 Task 10: Capturing Discriminative Attributes [PDF]
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018 This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that ‘urine’ is a discriminating feature in the word pair ‘kidney’, ‘bone’.
Alicia Krebs +2 more
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Graph Convolutional Network for Word Sense Disambiguation
Discrete Dynamics in Nature and Society, 2021 Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval.
Chun-Xiang Zhang +3 more
doaj +1 more source
SemEval-2016 Task 3: Community Question Answering [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Similarity (B), and Question--External Comment Similarity (C).
Nakov, Preslav +7 more
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Sentiment Analysis in Twitter: A SemEval Perspective [PDF]
Proceedings of the 7th Workshop on Computational Approaches to
Subjectivity, Sentiment and Social Media Analysis, 2016 The recent rise of social media has greatly democratized content creation. Facebook, Twitter, Skype, Whatsapp and LiveJournal are now commonly used to share thoughts and opinions about anything in the surrounding world. This proliferation of social media content has created new opportunities to study public opinion, with Twitter being especially ...
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A Single Attention-Based Combination of CNN and RNN for Relation Classification
IEEE Access, 2019 As a vital task in natural language processing, relation classification aims to identify relation types between entities from texts. In this paper, we propose a novel Att-RCNN model to extract text features and classify relations by combining recurrent ...
Xiaoyu Guo +4 more
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JokeMeter at SemEval-2020 Task 7: Convolutional Humor [PDF]
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020 This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7. The system is based on convolutional neural network architecture. We investigate the system on the official dataset, and we provide more insight to model itself to see how the learned inner features look.
Martin Docekal +3 more
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On SemEval-2010 Japanese WSD Task
Journal of Natural Language Processing, 2011 An overview of the SemEval-2 Japanese WSD task is presented. The new characteristics of our task are (1) the task will use the first balanced Japanese sense-tagged corpus, and (2) the task will take into account not only the instances that have a sense in the given set but also the instances that have a sense that cannot be found in the set.
Manabu Okumura +3 more
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INRIASAC: Simple Hypernym Extraction Methods [PDF]
, 2015 Given a set of terms from a given domain, how can we structure them into a taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here we present our simple taxonomy structuring techniques which, despite their simplicity, ranked first
Grefenstette, Gregory
core +4 more sources