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Semantic Recommendation System for Bilingual Corpus of Academic Papers

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1357)

Abstract

We tested four methods of making document representations cross-lingual for the task of semantic search for the similar papers based on the corpus of papers from three Russian conferences on NLP: Dialogue, AIST and AINL. The pipeline consisted of three stages: preprocessing, word-by-word vectorisation using models obtained with various methods to map vectors from two independent vector spaces to a common one, and search for the most similar papers based on the cosine similarity of text vectors. The four methods used can be grouped into two approaches: 1) aligning two pretrained monolingual word embedding models with a bilingual dictionary on our own (for example, with the VecMap algorithm) and 2) using pre-aligned cross-lingual word embedding models (MUSE). To find out, which approach brings more benefit to the task, we conducted a manual evaluation of the results and calculated the average precision of recommendations for all the methods mentioned above. MUSE turned out to have the highest search relevance, but the other methods produced more recommendations in a language other than the one of the target paper.

Keywords

Semantic similarity Semantic search Scientific literature search Document representations Cross-lingual representations 

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.University of OsloOsloNorway
  3. 3.Skolkovo Institute of Science and Technology (Skoltech)MoscowRussia

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