Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)

FST-Based Natural Language Processing Method for Opinion Extraction

Authors
Delin Liu, Haopeng Chen
Corresponding Author
Delin Liu
Available Online December 2016.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-265-7
ISSN
2352-538X
DOI
https://doi.org/10.2991/iceeecs-16.2016.111How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.111
DO  - https://doi.org/10.2991/iceeecs-16.2016.111
ID  - Liu2016/12
ER  -