Results 21 to 30 of about 14,560 (254)
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications [PDF]
, 2017 We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.
Augenstein, Isabelle +4 more
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BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs [PDF]
arXiv, 2017 In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings.
exaly +1 more source
SemEval-2015 Task 12: Aspect Based Sentiment Analysis [PDF]
, 2015 Maria Pontiki +4 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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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
SemEval-2020 Task 5: Counterfactual Recognition [PDF]
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020 We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent).
Huasha Zhao +5 more
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SemEval-2021 Task 1: Lexical Complexity Prediction [PDF]
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), 2021 This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five ...
Matthew Shardlow +3 more
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Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study
Sukkur IBA Journal of Computing and Mathematical Sciences, 2020 The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews.
Sindhu Abro +4 more
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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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