Automatic Labeling of Semantic Roles

Bibliographic Data

ID5809176
AuthorsDaniel Gildea (0000-0002-7858-2624, University of California, Berkeley, and International Computer Science Institute, Currently at Institute for Research in Cognitive Science, University of Pennsylvania, 3401 Walnut Street, Suite 400A, Philadelphia, PA 19104., corresponding author), Daniel Jurafsky (University of Colorado, Boulder, Departments of Linguistics and Computer Science, University of Colorado, Boulder, CO 80309., corresponding author)
Year2002
Volume28
Issue3
Pages245-288
Publication date2002-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueComputational Linguistics (JOURNAL)
Journal identifiersISSN: 0891-2017 • E-ISSN: 1530-9312
PublisherAssociation for Computational Linguistics (PUBLISHER • US)
DOI10.1162/089120102760275983
OpenAlexW2151170651
LanguageEN
Citations received44
References cited14

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 statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers. Our system achieves 82% accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65% precision and 61% recall. Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data

FrameNet · Predicate (mathematical logic · Semantic role labeling · Natural Language Processing Techniques · Speech and dialogue systems · Topic Modeling

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