Baseball: an automatic question answerer
Computers & thought
Natural language question-answering systems: 1969
Communications of the ACM
Automatic labeling of semantic roles
Computational Linguistics
Semantic grammar: a technique for efficient language understanding in limited domains.
Semantic grammar: a technique for efficient language understanding in limited domains.
Script application: computer understanding of newspaper stories.
Script application: computer understanding of newspaper stories.
Answering what-is questions by Virtual Annotation
HLT '01 Proceedings of the first international conference on Human language technology research
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
Addressing ontology-based question answering with collections of user queries
Information Processing and Management: an International Journal
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
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts
Knowledge-Based Systems
Information Processing and Management: an International Journal
A semantic role labelling-based framework for learning ontologies from Spanish documents
Expert Systems with Applications: An International Journal
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This paper presents two proposals based on semantic information, semantic roles and WordNet, for the answer extraction module of a general open-domain question answering (QA) system. The main objective of this research is to determine how the system performance is influenced by using this kind of information, and compare it with that of current QA systems based on named entities (NEs). NE-based QA systems achieve good results with NE-based questions. However, with common noun (CN) based questions, like ''Where is the stomach? In the abdomen'', they fail, and this is the main reason for our study. In this paper our new proposals for answering different types of questions are evaluated and compared with an NE-based approach for both NE-based and CN-based questions. From the results obtained it may be concluded that, with the aid of our proposals, the QA system performs much better with CN-based questions when semantic information is used (semantic information F"@b"="1=74.73% vs. NEF"@b"="1=12.19%). Moreover, the more semantic information the system uses, the better the precision and correctness of the answer it achieves.