International Conference on Knowledge Engineering and the Semantic Web

Knowledge Engineering and Semantic Web pp 158-167 | Cite as

Aspect Extraction from Reviews Using Conditional Random Fields

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 518)

Abstract

This paper describes an information extraction and content analysis system. The proposed system is based on a conditional random field algorithm and intended to extract aspect terms mentioned in the text. We use a set of morphological features for machine learning. The system is used for automatic extraction of explicit aspects and also to automatic extraction of all aspects (explicit, implicit and sentiment facts), and tested on two domains: restaurants and automobiles. We show that our system can produce quite a high level of precision which means that the system is capable of recognizing aspect terms rather accurately. The system demonstrates that even a small set of features for conditional random field algorithm can perform competitively and shows good results.

Keywords

Aspect detection Aspect extraction CRF Information retrieval Information extraction Content analysis 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.A.P. Ershov Institute of Informatics SystemsSiberian Branch of the Russian Academy of SciencesNovosibirskRussia
  2. 2.Independent DeveloperNovokuznetskRussia

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