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Autor*innen: Dhruva, Neil; Ferschke, Oliver; Gurevych, Iryna
Titel: Solving open-domain multiple choice questions with textual entailment and text similarity measures
Aus: Cappellato, Linda;Ferro, Nicola;Halvey, Martin;Kraaij, Wessel (Hrsg.): CLEF2014 Working Notes: Working Notes for the CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014, Aachen: RWTH, 2014 (Workshop Proceedings, 1180), S. 1375-1385
URN: urn:nbn:de:0074-1180-0
URL: http://ceur-ws.org/Vol-1180/CLEF2014wn-QA-DhruvaEt2014.pdf
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Antwort; Computerlinguistik; Computerunterstütztes Verfahren; Frage; Leseverstehen; Multiple-Choice-Verfahren; Textverständnis
Abstract: In this paper, we present a system for automatically answering open-domain, multiple choice reading comprehension questions about short English narrative texts. The system is based on state-of-the-art text similarity measures, textual entailment metrics and coreference resolution and does not make use of any additional domain specific background knowledge. Each answer option is scored with a combination of all evaluation metrics and ranked according to their overall score in order to determine the most likely correct answer. Our best configuration achieved the second highest score across all competing system in the entrance exam grading challenge with a c@1 score of 0.375. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung