Cross-lingual similar document retrieval methods
https://doi.org/10.15514/ISPRAS-2019-31(5)-9
Abstract
In this paper, we compare different methods for cross-lingual similar document retrieval. We focus on Russian-English language pair. We compare well-known methods like Cross Lingual Explicit Semantic Analysis (CL-ESA) with methods based on cross-lingual embeddings. We use approximate nearest neighbor (ANN) search to retrieve documents based entirely on distances between learned document embeddings. Also we employ a more traditional approach with usage of inverted index, with extra step of mapping top keywords from one language to other with the help of cross-lingual word embeddings. We use Russian-English aligned Wikipedia articles to evaluate all approaches. Conducted experiments show that an approach with inverted index achieves better performance in terms of recall and MAP than other methods.
Keywords
Supporting agencies: This study was funded by RFBR according to the research project № 18-37-20017. The reported research is also partially funded by the project “Text mining tools for big data” as a part of the program supporting Technical Leadership Centers of the National Technological Initiative “Center for Big Data Storage and Processing” at the Moscow State University (Agreement with Fund supporting the NTI-projects No. 13/1251/2018 11.12.2018).
About the Authors
Denis Vladimirovich Zubarev
FRC CSC RAS
Russian Federation
Russian Federation
Engineer of FRC CSC RAS.
Ilya Vladimirovich Sochenkov
FRC CSC RAS
Russian Federation
Russian Federation
PhD, Head of the Department of Intelligent Technologies and Systems of FRC CSC RAS.
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Review
For citations:
Zubarev D.V., Sochenkov I.V. Cross-lingual similar document retrieval methods. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2019;31(5):127-136. https://doi.org/10.15514/ISPRAS-2019-31(5)-9
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