Results 31 to 40 of about 1,185,215 (298)

Overview of word embedding-based virtual screening (WEBVS).

, 2023
Overview of word embedding-based virtual screening (WEBVS).
Makiko Fujiwara (15470868), Kazuyuki Miyai (15470883), Kazuhito Hayashi (15470862), Takeshi Ogusu (15470877), Shin-ichi Tokumoto (15470880), Mayumi Nakashima (15470874), Akihiko Shigemoto (15470865), Megumi Mori (468309), Hiroaki Yabuuchi (61222), Yuhei Nomura (15470871)   +9 more
core   +1 more source

Word Embedding Methods in Natural Language Processing: a Review [PDF]

Jisuanji kexue yu tansuo
Word 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

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, Xin Jin, Jianyue Ni, Baogang Wei, Zhiguo Lu   +4 more
doaj   +1 more source

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, Opim Salim Sitompul, Teddy Mantoro, Erna Budhiarti Nababan   +3 more
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, Teja Kanchinadam, Devin Conathan, Glenn Fung   +3 more
doaj   +1 more source

Statistics of matched words in each word embedding.

, 2023
Statistics of matched words in each word embedding.
Khadija Akter Lima (17027752), Sidratul Montaha (13200394), Mirjam Jonkman (9765452), Khan Md Hasib (15946547), Asif Karim (4419889), Sami Azam (9765449), Sheak Rashed Haider Noori (10533015)   +6 more
core   +1 more source

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
doaj   +1 more source

Activation of embedded words in spoken word recognition. [PDF]

Journal of Experimental Psychology: Human Perception and Performance, 1997
info:eu-repo/semantics ...
Vroomen, Jean, De Gelder, Béatrice
openaire   +3 more sources

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
doaj   +1 more source

Visual Word Embedding for Text Classification

, 2021
The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document ...
Gallo I., Landro N., La Grassa R., Nawaz S., Shah Nawaz, Ignazio Gallo, Nicola Landro, Riccardo La Grassainst   +7 more
core   +1 more source