Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

The QUANTUM Question Answering System at TREC-11

This year, we participated to the Qnestion Answering task ibr the second time with the QUANTUM system. Vv'e en- tered 2 runs for the main task (one using the web, the other without) and i run for the list task (without the web). e essentially built on last year's experience to enhance the system. The a'chitecture of QUANTUM is mainly the same as last year: it uses patterns that rely on shallow parsing techniques and regular expressions to analyze the question and then select the most appropriate extraction function. This extraction function is then applied o one-paragraph long passages retrieved by Okapi to extract and score candidate ansvers. Among the novelties we added to QUANTUM this year is a web module that finds exact answers using high-precision refbrmulation of the question to anticipate the expected context of the answer

The QUANTUM Question Answering System at TREC-11

This year, we participated to the Question Answering task for the second time with the QUANTUM system. We entered 2 runs for the main task (one using the web, the other without) and 1 run for the list task (without the web). We essentially built on last year’s experience to enhance the system. The architecture of QUANTUM is mainly the same as last year: it uses patterns that rely on shallow parsing techniques and regular expressions to analyze the question and then select the most appropriate extraction function. This extraction function is then applied to one-paragraph long passages retrieved by Okapi to extract and score candidate answers. Among the novelties we added to QUANTUM this year is a web module that finds exact answers using high-precision reformulation of the question to anticipate the expected context of the answer