Using semantic role labeling to predict answer types
Författare
Summary, in English
Most question answering systems feature a step to predict an expected answer type given a question. Li and Roth \cite{li2002learning} proposed an oft-cited taxonomy to the categorize the answer types as well as an annotated data set. While offering a framework compatible with supervised learning, this method builds on a fixed and rigid model that has to be updated when the question-answering domain changes. More recently, Pinchak and Lin \cite{pinchak2006} designed a dynamic method using a syntactic model of the answers that proved more versatile. They used syntactic dependencies to model the question context and evaluated the performance on an English corpus. However, syntactic properties may vary across languages and techniques applicable to English may fail with other languages. In this paper, we present a method for constructing a probability-based answer type model for each different question. We adapted and reproduced the original experiment of Pinchak and Lin \cite{pinchak2006} on a Chinese corpus and we extended their model to semantic dependencies. Our model evaluates the probability that a candidate answer fits into the semantic context of a given question. We carried out an evaluation on a set of questions either drawn from NTCIR corpus \cite{ntcir2005} or that we created manually.
Avdelning/ar
Publiceringsår
2014
Språk
Engelska
Sidor
29-31
Publikation/Tidskrift/Serie
ESAIR '14 Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval
Länkar
Dokumenttyp
Konferensbidrag
Förlag
Association for Computing Machinery (ACM)
Ämne
- Computer Science
Conference name
the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR ’14
Conference date
2014-11-03 - 2014-11-07
Conference place
Shanghai, China
Status
Published
ISBN/ISSN/Övrigt
- ISBN: 978-1-4503-1365-0