Difference between revisions of "Semantic Role Labeling"
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[A0 He ] [AM-MOD would ] [AM-NEG n't ] [V accept ] [A1 anything of value ] from [A2 those he was writing about ] . | [A0 He ] [AM-MOD would ] [AM-NEG n't ] [V accept ] [A1 anything of value ] from [A2 those he was writing about ] . | ||
| − | == | + | == Existing corpora == |
* [[FrameNet]] | * [[FrameNet]] | ||
* PropBank | * PropBank | ||
Revision as of 04:19, 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 and Question Answering.
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
- FrameNet
- PropBank
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.
