Difference between revisions of "Semantic Role Labeling"
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This is a technical [[Category::Problem|problem]] related to one of the term projects in [[Information Extraction 10-707 in Fall 2010]]. | This is a technical [[Category::Problem|problem]] related to one of the term projects in [[Information Extraction 10-707 in Fall 2010]]. | ||
| − | Semantic role labeling is a task of detecting the semantic arguments of a sentence. Typical semantic arguments are usually about roles related with the predicate or verb of a sentence such as agent, patient, and instrument. Recognizing and labeling semantic arguments is relatively domain-independent, and could be important in all NLP tasks such like Information extraction | + | Semantic role labeling is a task of detecting the semantic arguments of a sentence. Typical semantic arguments are usually about roles related with the predicate or verb of a sentence such as agent, patient, and instrument. Recognizing and labeling semantic arguments is relatively domain-independent, and could be important in all NLP tasks such like Information extraction, Question Answering, Machine Translation, and Document Summarization. |
==History== | ==History== | ||
Revision as of 04:38, 1 November 2010
This is a technical problem related to one of the term projects in Information Extraction 10-707 in Fall 2010.
Semantic role labeling is a task of detecting the semantic arguments of a sentence. Typical semantic arguments are usually about roles related with the predicate or verb of a sentence such as agent, patient, and instrument. Recognizing and labeling semantic arguments is relatively domain-independent, and could be important in all NLP tasks such like Information extraction, Question Answering, Machine Translation, and Document Summarization.
Contents
History
Semantic role labeling has been studied as shared tasks at CoNLL-2004 and CoNLL-2005.
Details
The following sentence, taken from the PropBank corpus, shows the semantic role labeling.
[A0 He ] [AM-MOD would ] [AM-NEG n't ] [V accept ] [A1 anything of value ] from [A2 those he was writing about ] .
Existing corpora
State of the art
- rely on hand-developed grammars
- data-driven techniques
Related Paper
The Gildea and Jurafsky Computational Linguistics 2002 is a paper solving the semantic role labeling problem.
