NDLI: Automatic Labeling of Semantic Role on Chinese FrameNet Using Conditional Random Fields

Content Provider	IEEE Xplore Digital Library
Author	Li, Jihong Wang, Ruibo Wang, Weilin Gu, Bo Li, Guochen
Copyright Year	2009
Abstract	Given an input sentence and a target word and its frame, automatic semantic role labeling on the Chinese FrameNet (CFN) database can be divided into two subtasks. One is identification of the boundaries of semantic roles, and the other is the classification of semantic roles. In this paper, the tasks are regarded as a sequential tagging problem in the sentence at word-level, so the model of conditional random fields was adopted. The best feature templates of the model were chosen by applying orthogonal arrays. The training and testing data sets consist of the sample sentences of 25 frames selected from the current CFN corpus. The experimental results in our test data set show that the F-measure for identifying boundaries of semantic roles reached 70.42\%, and the accuracy in classifying semantic roles achieved 80.4\%, but when the two subtasks were sequential automatically processed, the result was 59.48\% F-measure.
Starting Page	259
Ending Page	262
File Size	368289
Page Count	4
File Format	PDF
ISBN	9780769538013
DOI	10.1109/WI-IAT.2009.277
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	conditional random fields Conferences Information technology Intelligent agent Semantic Web Sequential analysis semantic role labeling Automatic testing Tagging Natural language processing Labeling Deductive databases Chinese FrameNet
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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