Results 1 to 10 of about 256,379 (183)
Sentiment-Aware Word Embedding for Emotion Classification
Applied Sciences, 2019 Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating
Xingliang Mao +4 more
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Impact of word embedding models on text analytics in deep learning environment: a review [PDF]
Artificial Intelligence Review, 2023 Deepak Asudani, Nknagwani, Pradeep Singh
exaly +2 more sources
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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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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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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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 ...
Cotterell, Ryan, Schütze, Hinrich
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Relational Word Embeddings [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding.
Camacho Collados, Jose +2 more
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Comparing general and specialized word embeddings for biomedical named entity recognition [PDF]
PeerJ Computer Science, 2021 Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used.
Rigo E. Ramos-Vargas +2 more
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Semantic Role Labeling for Amharic Text Using Multiple Embeddings and Deep Neural Network
IEEE Access, 2023 Amharic is morphologically complex and under-resourced language, posing difficulties in the development of natural language processing applications.
Bemnet Meresa Hailu +2 more
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