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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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Data Sets: Word Embeddings Learned from Tweets and General Data
, 2017 A word embedding is a low-dimensional, dense and real- valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually gener- ated from a large text corpus.
Li, Quanzhi +3 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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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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Relevance-based Word Embedding
, 2017 Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based
Ai Qingyao +15 more
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Using Word Embeddings in Twitter Election Classification [PDF]
, 2016 Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification.
Macdonald, Craig +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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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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Improving Word Embedding Using Variational Dropout
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings.
Zainab Albujasim +3 more
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Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
, 2018 Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings.
Kann, Katharina +2 more
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