LCC Tools for Question Answering.
DAN I. MOLDOVAN, Sanda M. Harabagiu, Roxana Gîrju, Paul Morǎrescu, V. Finley Lacatusu, Adrian Novischi, Adriana Badulescu, Orest Bolohan · 2002
The increased complexity of the TREC QA questions requires advanced text processing tools that rely on natural language processing and knowledge reasoning. This paper presents the suite of tools that account for the performance of the PowerAnswer question answering system. It is shown how questions, answers and world knowledge are transformed first in logic representation, followed by a systematic and rigorous logic proof that validly answers questions posed to the QA system. At TREC QA 2002, PowerAnswer obtained a confidence-weighted score of 0.856, answering correctly 415 out of 500 questions.