Lecture 19: Question Answering

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14. Question-Answering (QA)

Presentation transcript:

Lecture 19: Question Answering Kai-Wei Chang CS @ University of Virginia kw@kwchang.net Couse webpage: http://kwchang.net/teaching/NLP16 CS6501-NLP

CS6501– Natural Language Processing Question answering credit: ifunny.com 'Watson' computer wins at 'Jeopardy' CS6501– Natural Language Processing

CS6501– Natural Language Processing Question answering Go beyond search CS6501– Natural Language Processing

Go beyond traditional web search Answer queries using structured knowledge Google Knowledge Graph Facebook Graph Search Bing’s Satori Need to understand the query! The following slides are modified from Christopher Manning & Pandu Nayak’s intro. to IR. CS6501-NLP

Example from Fernando Pereira (GOOG)

Paradigms for question answering Text-based approaches TREC QA, IBM Watson Structured knowledge-based approaches Apple Siri, Wolfram Alpha, Facebook Graph Search CS6501-NLP

Task – Answer Sentence Selection Given a factoid question, find the sentence that Contains the answer Can sufficiently support the answer Q: Who won the best actor Oscar in 1973? S1: Jack Lemmon was awarded the Best Actor Oscar for Save the Tiger (1973). S2: Academy award winner Kevin Spacey said that Jack Lemmon is remembered as always making time for others. Scott Wen-tau Yih (ACL 2013) paper

Who won the best actor Oscar in 1973? Lemmon was awarded the Best Supporting Actor Oscar in 1956 for Mister Roberts (1955) and the Best Actor Oscar for Save the Tiger (1973), becoming the first actor to achieve this rare double… Source: Jack Lemmon -- Wikipedia

Word Alignment for Question Answering TREC QA (1999-2005) What is the fastest car in the world? The Jaguar XJ220 is the dearest, fastest and most sought after car on the planet. Word Alignment for Question Answering TREC QA (1999-2005) [Harabagiu & Moldovan, 2001] Assume that there is an underlying alignment Describes which words in and can be associated See if the (syntactic/semantic) relations support the answer

Knowledge-based QA Build a semantic representation of the query Times, dates, locations, entities, numeric quantities Map from this semantics to query structured data or resources Database Ontologies (Wikipedia infoboxes, dbPedia, WordNet, Yago) Scientific databases

Linking Open Data cloud Diagram http://lod-cloud.net/ CS6501-NLP