Results 21 to 30 of about 31,081 (304)
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
openaire +4 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
Context-word region embedding method.
, 2022 Context-word region embedding method.
Saranya Maneeroj (12506894) +2 more
core +1 more source