Results 51 to 60 of about 1,185,215 (298)
Word Embedding With Zipf’s Context
IEEE Access, 2019 Word embeddings generated by neural language models have achieved great success in many NLP tasks. However, neural language models may be difficult to train and time consuming.
Lizheng Gao +3 more
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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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, 2015 Code for the ACL-2015 paper "Accurate Linear-Time Chinese Word Segmentation via Embedding ...
JM
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Word Embedding Techniques for Malware Classification [PDF]
, 2020 Word embeddings are often used in natural language processing as a means to quantify relationships between words. More generally, these same word embedding techniques can be used to quantify relationships between features.
Chandak, Aniket
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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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Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts
Frontiers in Big Data, 2020 Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing ...
Yu Meng +5 more
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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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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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Improving Word Embedding Using Variational Dropout
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings.
Zainab Albujasim +3 more
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Biomedical Word Sense Disambiguation with Word Embeddings [PDF]
, 2017 There is a growing need for automatic extraction of information and knowledge from the increasing amount of biomedical and clinical data produced, namely in textual form. Natural language processing comes in this direction, helping in tasks such as information extraction and information retrieval.
Rui Antunes 0002, Sérgio Matos
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