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FPT University|e-Resources > Đồ án tốt nghiệp (Dissertations) > Công nghệ thông tin - Kỹ thuật phần mềm >
Please use this identifier to cite or link to this item: http://ds.libol.fpt.edu.vn/handle/123456789/1539

Title: Building a Semantic Role Labelling Toolkit for Vietnamese
Authors: Phuong, Le Hong
Hoang, Pham Thai
Khoai, Pham Xuan
Keywords: Capstone Project
Đồ án tốt nghiệp
Support Vector Machine,
Semantic Role labelling
Maximum Entropy
Natural Language Processing
Vietnamese
SRL
Issue Date: 11-Mar-2016
Abstract: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this thesis, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.
URI: http://ds.libol.fpt.edu.vn/handle/123456789/1539
Appears in Collections:Công nghệ thông tin - Kỹ thuật phần mềm

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