Automatic labeling of semantic roles
Computational Linguistics
Target word detection and semantic role chunking using support vector machines
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
A semantic approach to boost passage retrieval effectiveness for question answering
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Question answering based on semantic structures
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Corpus-based semantic role approach in information retrieval
Data & Knowledge Engineering
Question answering based on semantic roles
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
The role of verb sense disambiguation in semantic role labeling
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Using semantic constraints to improve question answering
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
A hybrid question answering schema using encapsulated semantics in lexical resources
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Temporal expression identification based on semantic roles
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
| Hi-index | 0.00 |
In recent years, improvements on automatic semantic role labeling have grown the interest of researchers in its application to different NLP fields, specially to QA systems. We present a proposal of automatic generalization of the use of SR in QA systems to extract answers for different types of questions. Firstly, we have implemented two different versions of an answer extraction module using SR: a) rules-based, and b) patterns-based. These modules work as part of a QA system to extract answers for location questions. Secondly, these approaches have been automatically generalized to any type of factoid questions using generalization rules. The whole system has been evaluated using both location and temporal questions from TREC datasets. Results indicate that an automatic generalization is feasible, obtaining same quality results for both original type of questions and new auto-generalized one (Precision: 88.20% LOC and 95.08% TMP). Furthermore, results show that patterns-based approach works better in both types of questions (F1 improvement + 40.88% LOCand + 15.41% TMP).