Proceedings ArticleDOI
Construction of a word similarity dataset and evaluation of word similarity techniques for Vietnamese
Bui Van Tan,Nguyen Phuong Thai,Pham Van Lam +2 more
- pp 65-70
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TLDR
In this article, the authors constructed a benchmark dataset for evaluation of similar techniques to the Vietnamese language, and experiment with some similarity techniques based on WordNet and word embeddings, and propose an extension for Lesk algorithm in order to improve the efficiency of similar measuring with Vietnamese language.Abstract:
Measuring word similarity is a core issue because it has many applications in natural language processing. Although many studies have been reported and the techniques have been developed for addressing this issue for English, however, the study dealing with the applications, analyses and evaluation word similarity techniques to Vietnamese still has not reported yet. Especially, there is still lack of the benchmark Vietnamese dataset for evaluating these techniques. In this paper, we report three main topics including: firstly, construct a benchmark dataset for evaluation of similar techniques to the Vietnamese language; secondly, experiment with some similarity techniques based on WordNet and word embeddings; and finally, propose an extension for Lesk algorithm in order to improving the efficiency of similar measuring with Vietnamese language.read more
Citations
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A survey of semantic relatedness evaluation datasets and procedures
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A Neural Network Model for Efficient Antonymy-Synonymy Classification by Exploiting Co-occurrence Contexts and Word-Structure Patterns
TL;DR: A deep neural network model (DVASNet) is proposed that can utilize not only embedding representations of words but also co-occurrence contexts and specific patterns of Vietnamese word structure and achieved significant improvements in comparison with a number of state-of-the-art methods.
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Enhancing Performance of Lexical Entailment Recognition for Vietnamese based on Exploiting Lexical Structure Features
TL;DR: This study proposes a novel method (VLER) for lexical entailment recognition problem on Vietnamese that first exploits lexical structure information of words as a feature, then combines this feature with vectors representation of words such as a unique feature for recognizing the relation.
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Combining Specialized Word Embeddings and Subword Semantic Features for Lexical Entailment Recognition
TL;DR: The authors proposed a method called LERC (Lexical Entailment Recognition Recognition Combination) to solve the problem by combining embedding representations and subword semantic features, which outperformed several methods published recently.
Book ChapterDOI
Antonyms-Synonyms Discrimination Based on Exploiting Rich Vietnamese Features.
TL;DR: A framework which exploits exhaustively special Vietnamese features to distinguish between antonyms from synonyms is introduced and a deep neural network model (ViASNet) is proposed that can utilize not only lexico-syntactic information captured from the context of word pairs in a corpus but also its word-level features, and distribution features as well.
References
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