Explicit Semantic Analysis as a Means for Topic Labelling

E-hypertext Media Topic Model with Automatic Label Assignment

“…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.…”

“…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.…”

“…First, we performed annotation of label triplets generated by Yandex-based algorithm and ESA. The annotation was based on a questionnaire modified from [ 24 , 26 ]. Two assessors were asked to mark label triplets according to 3-value scale of weights: «0» – a triplet doesn’t cover the content of a topic and can’t be used as a topic label; «1» – a triplet somehow reflects the content of a topic and to more or less extent can be used as a topic label; «2» – a triplet covers the content of a topic and can be used as a topic label.…”

“…We calculated averaged weights for Labels-Yandex and for Labels-ESA: w (Labels-Yandex) = 0,68, w (Labels-ESA) = 0,39, medium weight 0,54 (cf. in experiments on the corpus of Russian encyclopedic texts on linguistics [ 24 ] w (Labels-Yandex) = 0,54, w (Labels-ESA) = 0,47, medium weight 0,51). Labels-Yandex performs better than Labels-ESA as it addresses to a wide range of web-documents of various subject areas, styles and genres, while ESA uses Wikipedia texts which reveal the traits of scientific style.…”

Topic Modeling in Russia: Current Approaches and Issues in Methodology

“…Beside this, several works by computer linguists have suggested decisions for the Russian language, including adding automated labeling to Russian-language topics (Mirzagitova and Mitrofanova 2016) and showing the possibility of domain term extraction by topic modeling (Bolshakova et al 2013). Automatic topic labeling by a single word or phrase is expected to ease topic interpretation; working upon it continued in the recent years by comparing quality of two labeling algorithms, namely the vector-based Explicit Semantic Analysis (ESA) and graph-based method, with the former one preferred by the authors (Kriukova et al 2018).…”