Results 41 to 50 of about 89,062 (279)
Cultural Cartography with Word Embeddings [PDF]
Poetics, 2020 Using the frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized and continuous “meaning-space” where words are assigned a location based on relations of similarity to other words ...
Stoltz, Dustin, Taylor, Marshall
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Information, 2015 Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts.
Shengyu Liu +3 more
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Better Word Embeddings by Disentangling Contextual n-Gram Information
, 2019 Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings.
Gupta, Prakhar +2 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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SPINE: SParse Interpretable Neural Embeddings
, 2017 Prediction without justification has limited utility. Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations.
Berg-Kirkpatrick, Taylor +4 more
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Gloss Alignment using Word Embeddings
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023 Capturing and annotating Sign language datasets is a time consuming and costly process. Current datasets are orders of magnitude too small to successfully train unconstrained \acf{slt} models. As a result, research has turned to TV broadcast content as a source of large-scale training data, consisting of both the sign language interpreter and the ...
Walsh, Harry +3 more
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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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, 2021 This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds. This metric is employed to learn a continuous vector embedding space that groups similar sounding words together and can be used for various downstream computational phonology tasks.
Sharma, Rahul +2 more
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Closed Form Word Embedding Alignment [PDF]
2019 IEEE International Conference on Data Mining (ICDM), 2019 We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two ...
Sunipa Dev +2 more
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