Results 31 to 40 of about 254,842 (278)
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity [PDF]
, 2017 We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns. The models we consider include an experiential model, based on crowd-sourced association data, several popular
Abnar, Samira +3 more
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Biomedical Word Sense Disambiguation with Word Embeddings [PDF]
, 2017 There is a growing need for automatic extraction of information and knowledge from the increasing amount of biomedical and clinical data produced, namely in textual form. Natural language processing comes in this direction, helping in tasks such as information extraction and information retrieval.
Antunes, Rui, Matos, Sérgio
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Bayesian estimation‐based sentiment word embedding model for sentiment analysis
CAAI Transactions on Intelligence Technology, 2022 Sentiment word embedding has been extensively studied and used in sentiment analysis tasks. However, most existing models have failed to differentiate high‐frequency and low‐frequency words.
Jingyao Tang +7 more
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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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A Polarity Capturing Sphere for Word to Vector Representation
Applied Sciences, 2020 Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications.
Sandra Rizkallah +2 more
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Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network [PDF]
Jisuanji kexue, 2021 Aspect extraction of the microblog involved in the case is a task in a specific domain.The expression of aspect words is diverse and the meaning is different from that of the general domain.Only relying on the word embedding in the general domain,these ...
WANG Xiao-han, TAN Chen-chen, XIANG Yan, YU Zheng-tao
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Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016 Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks.
Mou, Lili +5 more
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An Enhanced Neural Word Embedding Model for Transfer Learning
Applied Sciences, 2022 Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However,
Md. Kowsher +6 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
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Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification
Jurnal Riset Informatika, 2023 A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically.
Mukhamad Rizal Ilham, Arif Dwi Laksito
doaj +1 more source