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 ] . | ||
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| + | The unit of processing of Semantic Role Labeling is a sentence. Depth of semantics can be said shallow. Semantic Role Labeling covers broad domains, and mostly not connected to applications directly. | ||
== Existing corpora == | == Existing corpora == | ||
Revision as of 03:43, 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 ] .
The unit of processing of Semantic Role Labeling is a sentence. Depth of semantics can be said shallow. Semantic Role Labeling covers broad domains, and mostly not connected to applications directly.
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.
