FST-Based Natural Language Processing Method for Opinion Extraction

DOI

10.2991/iceeecs-16.2016.111How to use a DOI?

Keywords

NLP; Rule; Syntax Tree

Abstract

This paper proposes a rule-based and Finite State Transducers (FST) based NLP method for extracting information from massive text. The method differs from n-gram based popular method which relies on probability statistics and machine learning. In our method, the rules are grammars of a language, summarized by people. FST is the implementation tool of rules. It can process natural language and generate a syntax tree for each sentence. To support applying the rules, we tokenize and generate the stem of words, and find many word features which are recorded in a dictionary. After generating a syntax tree, we extract useful information on many aspects, such as subject-verb-object matches and opinion matches. We evaluate our system on the accuracy rate of the syntax trees, and show that the result is satisfactory.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Delin Liu
AU  - Haopeng Chen
PY  - 2016/12
DA  - 2016/12
TI  - FST-Based Natural Language Processing Method for Opinion Extraction
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 552
EP  - 556
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.111
DO  - 10.2991/iceeecs-16.2016.111
ID  - Liu2016/12
ER  -