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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zweigenbaum, Pierre Ben Abacha, Asma |
| Abstract | Designing question answering systems requires efficient and deep analysis of natural language questions. A key process for this task is to translate the semantic relations expressed in the question into a machine-readable representation. In this paper we tackle question analysis in the medical field. More precisely, we study how to translate a natural language question into a machine-readable representation. The underlying transformation process requires determining three key points: (i) What are the main characteristics of medical questions? (ii) Which methods are the most fitted for the extraction of these characteristics? and (iii) how to translate the extracted information into a machine-understandable representation? We present a complete question analysis approach including medical entity recognition, semantic relation extraction and automatic translation to SPARQL queries. Our study supports the fact that SPARQL can represent a wide range of natural language questions in a question-answering perspective. Experiments on a corpus of real questions show that we obtain encouraging results in medical entity recognition and relation extraction. The obtained results also show that the output SPARQL queries correctly represent more than 60% of the original questions. |
| Starting Page | 41 |
| Ending Page | 50 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450307819 |
| DOI | 10.1145/2110363.2110372 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-01-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Sparql Rdf Machine learning Medical question analysis Information extraction Question answering |
| Content Type | Text |
| Resource Type | Article |
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