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 Suresh Asudani +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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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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Benefiting from Structured Resources to Present a Computationally Efficient Word Embedding Method [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 In recent years, new word embedding methods have clearly improved the accuracy of NLP tasks. A review of the progress of these methods shows that the complexity of these models and the number of their training parameters grows increasingly.
F. Jafarinejad
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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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A word embedding trained on South African news data
The African Journal of Information and Communication, 2022 This article presents results from a study that developed and tested a word embedding trained on a dataset of South African news articles. A word embedding is an algorithm-generated word representation that can be used to analyse the corpus of words ...
Martin Canaan Mafunda +3 more
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Social Media Topic Recognition Based on Word Embedding and Probabilistic Topic Model [PDF]
Jisuanji gongcheng, 2017 Word embedding can capture the semantic information of words from the large corpus,and its combination with the probabilistic topic model can solve the problem of lack of semantic information in the standard topic model.So in this paper,Word-Topic ...
YU Chong,LI Jing,SUN Xudong,FU Xianghua
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