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Tatiana Litvinova,Polina Panicheva,Elena Kotlyarova,Victoria Zavarzina
Pág. 47 - 53
Development of the models of distributional semantics is one of the most important directions of research in modern NLP. This field is developing rapidly. New transformed-based models allow one to obtain good results in a lot of practical tasks, although...
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Pablo Gamallo
This article describes a compositional model based on syntactic dependencies which has been designed to build contextualized word vectors, by following linguistic principles related to the concept of selectional preferences. The compositional strategy pr...
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Stefan Helmstetter and Heiko Paulheim
The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is ...
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A. A. Antipenko,O. A. Mitrofanova
Pág. 27 - 33
The paper discusses results of the experiment on automatic extraction of associative relations from corpora of Russian texts from Facebook and Pikabu social networks by means of distributional semantic models. The choice of linguistic data for analysis, ...
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Dandan Li and Douglas Summers-Stay
Word embeddings have been very successful in many natural language processing tasks, but they characterize the meaning of a word/concept by uninterpretable ?context signatures?. Such a representation can render results obtained using embeddings difficult...
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Korawit Orkphol and Wu Yang
Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the context and sens...
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Hugo Gonçalo Oliveira
Identifying similar and related words is not only key in natural language understanding but also a suitable task for assessing the quality of computational resources that organise words and meanings of a language, compiled by different means. This paper,...
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