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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
RISE., Swedish ICT, SICS.
RISE., Swedish ICT, SICS.ORCID-id: 0000-0001-6949-6380
Vise andre og tillknytning
2010 (engelsk)Inngår i: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, 2010, 12Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper reports experiments for the CoNLL 2010 shared task on learning to detect hedges and their scope in natural language text. We have addressed the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection in the biological domain we use an L1-regularised binary support vector machine, while for sentence level weasel detection in the Wikipedia domain, we use an L2-regularised approach. We model the in-sentence uncertainty cue and scope detection task as an L2-regularised approximate maximum margin sequence labelling problem, using the BIO-encoding. In addition to surface level features, we use a variety of linguistic features based on a functional dependency analysis. A greedy forward selection strategy is used in exploring the large set of potential features. Our official results for Task 1 for the biological domain are 85.2 F1-score, for the Wikipedia set 55.4 F1-score. For Task 2, our official results are 2.1 for the entire task with a score of 62.5 for cue detection. After resolving errors and final bugs, our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes: 39.6 and cues: 78.5.

sted, utgiver, år, opplag, sider
2010, 12.
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-23722OAI: oai:DiVA.org:ri-23722DiVA, id: diva2:1042799
Konferanse
CoNLL 2010, July 2010, Uppsala, Sweden
Prosjekter
DISTRESS
Forskningsfinansiär
Swedish Research CouncilTilgjengelig fra: 2016-10-31 Laget: 2016-10-31 Sist oppdatert: 2018-03-08bibliografisk kontrollert

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