Results 31 to 40 of about 31,081 (304)
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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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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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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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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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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Analysis of Italian Word Embeddings [PDF]
, 2017 In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an extensive analysis and an evaluation, showing
Tripodi, Rocco, Pira, Stefano Li
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Bayesian estimation‐based sentiment word embedding model for sentiment analysis
CAAI Transactions on Intelligence Technology, 2022 Sentiment word embedding has been extensively studied and used in sentiment analysis tasks. However, most existing models have failed to differentiate high‐frequency and low‐frequency words.
Jingyao Tang +7 more
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Word Embeddings in Sentiment Analysis [PDF]
, 2018 In the late years sentiment analysis and its applications have reached growing popularity. Concerning this field of research, in the very late years machine learning and word representation learning derived from distributional semantics field (i.e. word embeddings) have proven to be very successful in performing sentiment analysis tasks.
Petrolito R, Dell'Orletta F
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Word Embedding as Maximum A Posteriori Estimation [PDF]
, 2019 The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem.
Schockart, Steven +5 more
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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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