Abstract
This paper deals with a method for topic labelling that makes use of Explicit Semantic Analysis (ESA). Top words of a topic are given to ESA as an input, and the algorithm yields titles of Wikipedia articles that are considered most relevant to the input. An alternative approach that serves as a strong baseline employs titles of first outputs in a search engine, given topic words as a query. In both methods, obtained titles are then automatically analysed and phrases characterizing the topic are constructed from them with the use of a graph algorithm and are assigned with weights. Within the proposed method based on ESA, post-processing is then performed to sort candidate labels according to empirically formulated rules. Experiments were conducted on a corpus of Russian encyclopaedic texts on linguistics. The results justify applying ESA for this task, and we state that though it works a little inferior to the method based on a search engine in terms of labels’ quality, it can be used as a reasonable alternative because it exhibits two advantages that the baseline method lacks.
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Kriukova, A., Erofeeva, A., Mitrofanova, O., Sukharev, K. (2018). Explicit Semantic Analysis as a Means for Topic Labelling. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_11
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