Results 11 to 20 of about 89,062 (279)
Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information
Applied Sciences, 2022 The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and ...
Dongsuk Oh, Jungwoo Lim, Heuiseok Lim
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Learned Text Representation for Amharic Information Retrieval and Natural Language Processing
Information, 2023 Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications
Tilahun Yeshambel +2 more
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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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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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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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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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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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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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Using Word Embeddings in Twitter Election Classification [PDF]
, 2016 Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification.
Macdonald, Craig +2 more
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