Results 51 to 60 of about 31,081 (304)
Compressing Word Embeddings [PDF]
, 2016 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental results were submitted to ICLR-2016, but the paper has been significantly updated, since a new experimental set-up worked much ...
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Towards Resolving Word Ambiguity with Word Embeddings
CoRR, 2023 Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g.
Matthias Thurnbauer +3 more
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, 2015 <p>preliminary release for the code accompanying the paper "Accurate Linear-Time Chinese Word Segmentation via Embedding Matching" (ACL-2015)</p ...
JM
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Compositional Demographic Word Embeddings [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations. While personalized embeddings can be useful to improve language model performance and other language processing tasks, they can only be computed for people with a ...
Charles Welch +3 more
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Citation Intent Classification Using Word Embedding
IEEE Access, 2021 Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations.
Muhammad Roman +4 more
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, 2017 In the proceedings of the International Conference on Machine Learning (ICML 2017); 8 pages + references and ...
Robert Bamler, Stephan Mandt
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The activation of embedded words in spoken word recognition [PDF]
Journal of Memory and Language, 2015 The current study investigated how listeners understand English words that have shorter words embedded in them. A series of auditory-auditory priming experiments assessed the activation of six types of embedded words (2 embedded positions × 3 embedded proportions) under different listening conditions.
Xujin, Zhang, Arthur G, Samuel
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Overcoming Poor Word Embeddings with Word Definitions [PDF]
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, 2021 Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models.
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A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
Applied Sciences, 2022 Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks.
Qizhi Li +4 more
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sentiment specific word embedding
, 2018 <p>sentiment specific word embedding learned based on the approach described in the following paper:</p> <p>D. Tang, et al., Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification, ACL 2014< ...
Tang
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