Results 31 to 40 of about 1,159,266 (295)
Word Embedding Methods in Natural Language Processing: a Review [PDF]
Jisuanji kexue yu tansuoWord embedding, as the first step in natural language processing (NLP) tasks, aims to transform input natural language text into numerical vectors, known as word vectors or distributed representations, which artificial intelligence models can process ...
ZENG Jun, WANG Ziwei, YU Yang, WEN Junhao, GAO Min
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Improve word embedding using both writing and pronunciation. [PDF]
PLoS ONE, 2018 Text representation can map text into a vector space for subsequent use in numerical calculations and processing tasks. Word embedding is an important component of text representation.
Wenhao Zhu +4 more
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Morpheme Embedding for Bahasa Indonesia Using Modified Byte Pair Encoding
IEEE Access, 2021 Word embedding is an efficient feature representation that carries semantic and syntactic information. Word embedding works as a word level that treats words as minor independent entity units and cannot handle words that are not in the training corpus ...
Amalia Amalia +3 more
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Context-word region embedding method.
, 2022 Context-word region embedding method.
Saranya Maneeroj (12506894) +2 more
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Task-Optimized Word Embeddings for Text Classification Representations
Frontiers in Applied Mathematics and Statistics, 2020 Word embeddings have introduced a compact and efficient way of representing text for further downstream natural language processing (NLP) tasks. Most word embedding algorithms are optimized at the word level.
Sukrat Gupta +3 more
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Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages
Information, 2019 To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model.
Yang Yuan, Xiao Li, Ya-Ting Yang
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Activation of embedded words in spoken word recognition. [PDF]
Journal of Experimental Psychology: Human Perception and Performance, 1997 Tilburg University Beatrice de Gelder Tilburg University and Universit6 Libre de Bruxelles Three cross-modal associative priming experiments investigated whether speech input acti- vates words that are embedded in other words.
Vroomen, Jean, De Gelder, Béatrice
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Skip-Gram-KR: Korean Word Embedding for Semantic Clustering
IEEE Access, 2019 Deep learning algorithms are used in various applications for pattern recognition, natural language processing, speech recognition, and so on. Recently, neural network-based natural language processing techniques use fixed length word embedding.
Sun-Young Ihm, Ji-Hye Lee, Young-Ho Park
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CoRR, 2019 This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra ...
Felipe Almeida, Geraldo Xexéo
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A Polarity Capturing Sphere for Word to Vector Representation
Applied Sciences, 2020 Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications.
Sandra Rizkallah +2 more
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