dc.contributor |
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.contributor |
Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.contributor.author |
Comas Umbert, Pere Ramon |
dc.contributor.author |
Turmo Borras, Jorge |
dc.contributor.author |
Màrquez Villodre, Lluís |
dc.date |
2012 |
dc.identifier.citation |
Comas, P.R.; Turmo, J.; Marquez, L. Sibyl, a factoid question answering system for spoken documents. "ACM transactions on information systems", 2012, vol. 30, núm. 3, p. 19:1-19:40. |
dc.identifier.citation |
1046-8188 |
dc.identifier.citation |
10.1145/2328967.2328972 |
dc.identifier.uri |
http://hdl.handle.net/2117/17486 |
dc.language.iso |
eng |
dc.relation |
http://dl.acm.org/citation.cfm?id=2328972&bnc=1 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural |
dc.subject |
Question-answering systems |
dc.subject |
Sibyl (Question-answering systems) |
dc.subject |
Natural language processing (Computer science) |
dc.subject |
Llenguatge natural (Informàtica) -- Processament |
dc.title |
Sibyl, a factoid question answering system for spoken documents |
dc.type |
info:eu-repo/semantics/publishedVersion |
dc.type |
info:eu-repo/semantics/article |
dc.description.abstract |
In this article, we present a factoid question-answering system, Sibyl, specifically tailored for question
answering (QA) on spoken-word documents. This work explores, for the first time, which techniques can be robustly adapted from the usual QA on written documents to the more difficult spoken document scenario.
More specifically, we study new information retrieval (IR) techniques designed or speech, and utilize several levels of linguistic information for the speech-based QA task. These include named-entity detection with phonetic information, syntactic parsing applied to speech transcripts, and the use of coreference resolution.
Sibyl is largely based on supervised machine-learning techniques, with special focus on the answer extraction step, and makes little use of handcrafted knowledge. Consequently, it should be easily adaptable to other
domains and languages. Sibyl and all its modules are extensively evaluated on the European Parliament Plenary Sessions English corpus, comparing manual with automatic transcripts obtained by three different
automatic speech recognition (ASR) systems that exhibit significantly different word error rates. This data belongs to the CLEF 2009 track for QA on speech transcripts. The main results confirm that syntactic
information is very useful for learning to rank question candidates, improving results on both manual and automatic transcripts, unless the ASR quality is very low. At the same time, our experiments on coreference
resolution reveal that the state-of-the-art technology is not mature enough to be effectively exploited for QA with spoken documents. Overall, the performance of Sibyl is comparable or better than the state-of-the-art on this corpus, confirming the validity of our approach. |
dc.description.abstract |
Peer Reviewed |