Results 21 to 30 of about 1,159,266 (295)
DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
Applied Sciences, 2020 Word embedding is an important reference for natural language processing tasks, which can generate distribution presentations of words based on many text data.
Shengwen Li +5 more
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Cultural Cartography with Word Embeddings [PDF]
Poetics, 2020 Using the frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized and continuous “meaning-space” where words are assigned a location based on relations of similarity to other words ...
Stoltz, Dustin, Taylor, Marshall
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TWE‐WSD: An effective topical word embedding based word sense disambiguation
CAAI Transactions on Intelligence Technology, 2021 Word embedding has been widely used in word sense disambiguation (WSD) and many other tasks in recent years for it can well represent the semantics of words.
Lianyin Jia +5 more
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Dual embedding with input embedding and output embedding for better word representation [PDF]
, 2022 Recent studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms
Jihoon Lee +5 more
core +1 more source
Word Embedding Techniques for Malware Classification [PDF]
, 2020 Word embeddings are often used in natural language processing as a means to quantify relationships between words. More generally, these same word embedding techniques can be used to quantify relationships between features.
Chandak, Aniket
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Joint Fine-Grained Components Continuously Enhance Chinese Word Embeddings
IEEE Access, 2019 The most common method of word embedding is to learn word vector representations from context information of large-scale text. However, Chinese words usually consist of characters, subcharacters, and strokes, and each part contains rich semantic ...
Chengyang Zhuang +3 more
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How Do Pronouns Affect Word Embedding
Tsinghua Science and Technology, 2017 Word embedding has drawn a lot of attention due to its usefulness in many NLP tasks. So far a handful of neural-network based word embedding algorithms have been proposed without considering the effects of pronouns in the training corpus.
Tonglee Chung +4 more
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Sentence model based subword embeddings for a dialog system
ETRI Journal, 2022 This study focuses on improving a word embedding model to enhance the performance of downstream tasks, such as those of dialog systems. To improve traditional word embedding models, such as skip-gram, it is critical to refine the word features and expand
Euisok Chung +2 more
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Name Entity Recognition for Military Based on Domain Adaptive Embedding [PDF]
Jisuanji kexue, 2022 In order to solve the poor quality problem of domain embedding space caused by inadequate military corpus which makes low accuracy of applying deep neural network model to military named entity recognition,this paper introduces a domain adaptive method ...
LIU Kai, ZHANG Hong-jun, CHEN Fei-qiong
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Survey of Word Embedding Models Research [PDF]
Jisuanji kexue yu tansuoAs a foundational technology in natural language processing, word embedding models map discrete linguistic symbols into continuous vector representations that computers can process.
WEN Yongqi, YANG Ruopeng, TAO Yu, ZHONG Yihao, HUANG Bo
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