Results 11 to 20 of about 1,159,266 (295)
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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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.
José Camacho-Collados +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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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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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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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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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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Socialized Word Embeddings [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Word embeddings have attracted a lot of attention. On social media, each user’s language use can be significantly affected by the user’s friends. In this paper, we propose a socialized word embedding algorithm which can consider both user’s personal characteristics of language use and the user’s social relationship on social media.
Ziqian Zeng +3 more
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CoRR, 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.
Rahul Sharma +2 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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