Automatic Labeling of Semantic Roles
نویسندگان
چکیده
منابع مشابه
Automatic Labeling of Semantic Roles
We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles such as AGENT or PATIENT, or more domain-specific semantic roles such as SPEAKER, MESSAGE, and TOPIC. The system is based on statist...
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The SENSEVAL-3 task to perform automatic labeling of semantic roles was designed to encourage research into and use of the FrameNet dataset. The task was based on the considerable expansion of the FrameNet data since the baseline study of automatic labeling of semantic roles by Gildea and Jurafsky. The FrameNet data provide an extensive body of “gold standard” data that can be used in lexical s...
متن کاملSenseval automatic labeling of semantic roles using Maximum Entropy models
As a task in SensEval-3, Automatic Labeling of Semantic Roles is to identify frame elements within a sentence and tag them with appropriate semantic roles given a sentence, a target word and its frame. We apply Maximum Entropy classification with feature sets of syntactic patterns from parse trees and officially attain 80.2% precision and 65.4% recall. When the frame element boundaries are give...
متن کاملAutomatic Labeling of Semantic Roles Qualifying Exam Proposal
The problem of linking syntactic constituents of a sentence to semantic roles is an essential part of many natural language processing tasks. The research outlined here aims to develop a statistical approach to the problem, extending the methodology that has been very successful in statistical parsing one step closer to language understanding. Both speci c and more abstract semantic roles are c...
متن کاملAutomatic Semantic Role Labeling
The goal of semantic role labeling is to map sentences to domain-independent semantic representations, which abstract away from syntactic structure and are important for deep NLP tasks such as question answering, textual entailment, and complex information extraction. Semantic role labeling has recently received significant interest in the natural language processing community. In this tutorial...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2002
ISSN: 0891-2017,1530-9312
DOI: 10.1162/089120102760275983