FST-Based Natural Language Processing Method for Opinion Extraction
Delin Liu, Haopeng Chen
Available Online December 2016.
- https://doi.org/10.2991/iceeecs-16.2016.111How to use a DOI?
- NLP; Rule; Syntax Tree
- 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.
- 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 PB - Atlantis Press SP - 552 EP - 556 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 -