Topical relevance relationships. I: why topic matching fails
Journal of the American Society for Information Science
Automatic labeling of semantic roles
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Semantic role labeling: an introduction to the special issue
Computational Linguistics
A combined memory-based semantic role labeler of English
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
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This paper presents a semantic role labeling system acting as the backbone of an application that monitors the contexts and relations in which a specified entity appears in written texts. Using semantic role analysis, we are able to answer questions such as: "What roles do entities play in different contexts?" or "When, why, where or how an event takes place?" Thus, by starting with an input entity, the system extracts the first 200 web pages found on a Google search for the particular entity, extracts relevant paragraphs that contain the entity, and then performs semantic role labeling on the extracted paragraphs. The main goal is to identify the role this entity plays, as well as the roles played by words frequently co-occurring with the input entity.