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Contextual Word Embedding [PDF]
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018 Effective clustering of short documents, such as tweets, is difficult because of the lack of sufficient semantic context. Word embedding is a technique that is effective in addressing this lack of semantic context. However, the process of word vector embedding, in turn, relies on the availability of sufficient contexts to learn the word associations ...
Debasis Ganguly, Kripabandhu Ghosh
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NEGATIVE-SAMPLING WORD-EMBEDDING METHOD
Scientific Journal of Astana IT University, 2022 One of the most famous authors of the method is Tomas Mikolov. His software and method of theoretical application are the major ones for our consideration today. It is better to pay attention that it is more mathematically oriented.
Madina Bokan
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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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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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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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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.
Sonkar, Shashank +2 more
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Approximating Word Ranking and Negative Sampling for Word Embedding [PDF]
, 2018 CBOW (Continuous Bag-Of-Words) is one of the most commonly used techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling ...
Guo, Guibing +3 more
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Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity [PDF]
, 2017 We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns. The models we consider include an experiential model, based on crowd-sourced association data, several popular
Abnar, Samira +3 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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