Using Explicit Semantic Analysis and Word2Vec in Measuring Semantic Relatedness of Russian Paraphrases

Kriukova, Anna; Mitrofanova, Olga; Sukharev, Kirill; Roschina, Natalia

doi:10.1007/978-3-030-02846-6_28

Cited by 2 publications

(2 citation statements)

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“…Our recent investigations are aimed to fill in the gap. In this study we used an ensemble of two graph-based methods using outer sources for candidate labels generation [24]: a) candidate labels extraction from Yandex search engine with their further ranking by TextRank (this is a graph-based model that takes into account the value of the each graph's vertex depending on how many links it forms) [25,26]; b) candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA) model [27,28]. These procedures comprise two stages: candidate extraction and ranking.…”

Section: Our Approach To Topic Labellingmentioning

confidence: 99%

“…Explicit Semantic Analysis (ESA) is a variety of distributional semantic models projecting words from Wikipedia dump to a high-dimensional vector space. The original ESA algorithm was developed to improve monolingual and crosslingual search [ 31 ], the paper [ 24 ] discusses the first experience in using it for label assignment, the papers [ 27 , 28 ] show its applications in detecting Russian text similarity/relatedness. In ESA model each article is treated as a separate «concept» represented by a vector generalizing all words co-occurring in an article.…”

Section: Our Approach To Topic Labellingmentioning

confidence: 99%

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E-hypertext Media Topic Model with Automatic Label Assignment

Mitrofanova

Kriukova

Shulginov

et al. 2021

Communications in Computer and Information Science

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This article deals with the principles of automatic label assignment for e-hypertext markup. We've identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label's triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms' results: at the first stage, two experts assessed the triplet's relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0»-a label doesn't match a topic; «1»-a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.

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Section: Our Approach To Topic Labellingmentioning

confidence: 99%

Section: Our Approach To Topic Labellingmentioning

confidence: 99%