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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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Word Embeddings through Hellinger PCA [PDF]
, 2014 Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming.
Ronan Collobert +3 more
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Mathematical Aspects of Word Embeddings [PDF]
, 2021 Word embeddings are a popular way of modelling relationships between words. Words are represented as low-dimensional vectors, such that the distances between the vectors reflect relationships between the words: words which are more similar to each other
Carrington, Rachel
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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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Understanding and Creating Word Embeddings
The Programming HistorianWord embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word ...
Avery Blankenship +2 more
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GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts
IEEE Access, 2018 Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications.
Wenxin Liang +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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Learning Chinese Word Embeddings With Words and Subcharacter N-Grams
IEEE Access, 2019 Co-occurrence information between words is the basis of training word embeddings; besides, Chinese characters are composed of subcharacters, words made up by the same characters or subcharacters usually have similar semantics, but this internal ...
Ruizhi Kang +4 more
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