Results 41 to 50 of about 259,269 (327)
Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification
Jurnal Riset Informatika, 2023 A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically.
Mukhamad Rizal Ilham, Arif Dwi Laksito
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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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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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Comparative Analysis of Word Embeddings for Capturing Word Similarities
, 2020 Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already ...
Kalajdjieski, Jovan +2 more
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Citation Intent Classification Using Word Embedding
IEEE Access, 2021 Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations.
Muhammad Roman +4 more
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Factors Influencing the Surprising Instability of Word Embeddings
, 2018 Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability.
Kummerfeld, Jonathan K. +2 more
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Gloss Alignment using Word Embeddings
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023 Capturing and annotating Sign language datasets is a time consuming and costly process. Current datasets are orders of magnitude too small to successfully train unconstrained \acf{slt} models. As a result, research has turned to TV broadcast content as a source of large-scale training data, consisting of both the sign language interpreter and the ...
Walsh, Harry +3 more
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Future Internet, 2018 Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel ...
Yue Wu, Junyi Zhang
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A Smaller and Better Word Embedding for Neural Machine Translation
IEEE Access, 2023 Word embeddings play an important role in Neural Machine Translation (NMT). However, it still has a series of problems such as ignoring the prior knowledge of the association between words, relying on specific task constraints passively in parameter ...
Qi Chen
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Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
, 2020 As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation.
Koç, Aykut +4 more
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