Results 21 to 30 of about 11,306 (258)
Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis
IEEE Access, 2021 Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields. Word embeddings play a significant role in sentiment analysis.
Yabing Wang +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
openaire +4 more sources
Slovene and Croatian word embeddings in terms of gender occupational analogies
Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave, 2021 In recent years, the use of deep neural networks and dense vector embeddings for text representation have led to excellent results in the field of computational understanding of natural language.
Matej Ulčar +3 more
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Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning can They Capture? [PDF]
Sociological Methods and ResearchAlina Arseniev-Koehler
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Learning linear transformations between counting-based and prediction-based word embeddings. [PDF]
PLoS ONE, 2017 Despite the growing interest in prediction-based word embedding learning methods, it remains unclear as to how the vector spaces learnt by the prediction-based methods differ from that of the counting-based methods, or whether one can be transformed into
Danushka Bollegala +2 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
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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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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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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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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
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