Results 31 to 40 of about 1,006,138 (295)
Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet.
International Journal of Interactive Multimedia and Artificial Intelligence, 2020 Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words.
Krishna Siva Prasad Mudigonda +1 more
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Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination [PDF]
PeerJ Computer Science, 2021 Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system.
Javeria Hassan +2 more
doaj +2 more sources
BMC Medical Informatics and Decision Making, 2018 Background In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretability.
Zhiwei Chen +3 more
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Creating Welsh Language Word Embeddings
Applied Sciences, 2021 Word embeddings are representations of words in a vector space that models semantic relationships between words by means of distance and direction. In this study, we adapted two existing methods, word2vec and fastText, to automatically learn Welsh word ...
Padraig Corcoran +4 more
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Unveiling Biases in Word Embeddings: An Algorithmic Approach for Comparative Analysis Based on Alignment [PDF]
, 2023 openWord embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora.
SANGUIN, PIETRO MARIA
core
Inteligencia Artificial, 2018 This work explores the use of word embeddings as features for Spanish verb sense disambiguation (VSD). This type of learning technique is named disjoint semisupervised learning: an unsupervised algorithm (i.e.
Cristian Cardellino +1 more
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Acoustic Word Embeddings for End-to-End Speech Synthesis
Applied Sciences, 2021 The most recent end-to-end speech synthesis systems use phonemes as acoustic input tokens and ignore the information about which word the phonemes come from.
Feiyu Shen, Chenpeng Du, Kai Yu
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 +3 more sources
FastText Spanish Medical Embeddings
, 2020 [Plan TL/medicine/word embeddings] Word embeddings generated from Spanish corpora that include: (a) the full-text in Spanish available in SciELO.org (until December/2018), (b) all articles from the following Wikipedia categories: Pharmacology, Pharmacy ...
Aitor Gonzalez-Agirre +5 more
core +1 more source
Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings
IEEE Access, 2021 We propose a lightweight algorithm of learning word single-meaning embeddings (WSME), by exploring WordNet synsets and Doc2vec document embeddings, to enhance the accuracy of semantic relatedness measurement.
Xiaotao Li, Shujuan You, Wai Chen
doaj +1 more source