Results 1 to 10 of about 24,099 (162)
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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An Enhanced Neural Word Embedding Model for Transfer Learning
Applied Sciences, 2022 Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However,
Md. Kowsher +6 more
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Impact of word embedding models on text analytics in deep learning environment: a review [PDF]
Artificial Intelligence Review, 2023 Naresh Kumar Nagwani +2 more
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Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020 Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice.
Orestis Papakyriakopoulos +3 more
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Sentiment Classification Performance Analysis Based on Glove Word Embedding
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2021 Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content.
Yasin Kırelli, Şebnem Özdemir
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Research of BERT Cross-Lingual Word Embedding Learning
Jisuanji kexue yu tansuo, 2021 With the development of multilingual information on the Internet, how to effectively represent the infor-mation contained in different language texts has become an important sub-task of natural language information processing.
WANG Yurong, LIN Min, LI Yanling
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Dynamic Contextualized Word Embeddings [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and ...
Hofmann, V +2 more
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Morphological Word-Embeddings [PDF]
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015 Published at NAACL ...
Ryan Cotterell, Hinrich Schütze
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Attention Word Embedding [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it.
Shashank Sonkar +2 more
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Mirroring Vector Space Embedding for New Words
IEEE Access, 2021 Most embedding models used in natural language processing require retraining of the entire model to obtain the embedding value of a new word. In the current system, as retraining is repeated, the amount of data used for learning gradually increases.
Jihye Kim, Ok-Ran Jeong
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