Filling Preposition-based Templates To Capture Information from Medical Abstracts
| dc.contributor.author | Leroy, Gondy | |
| dc.contributor.author | Chen, Hsinchun | |
| dc.date.accessioned | 2004-08-20T00:00:01Z | |
| dc.date.available | 2010-06-18T23:18:52Z | |
| dc.date.issued | 2002 | en_US |
| dc.date.submitted | 2004-08-20 | en_US |
| dc.identifier.citation | Filling Preposition-based Templates To Capture Information from Medical Abstracts 2002, :350-361 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105077 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.abstract | Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Medical Libraries | en_US |
| dc.subject | Information Extraction | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.subject.other | GeneScene | en_US |
| dc.title | Filling Preposition-based Templates To Capture Information from Medical Abstracts | en_US |
| dc.type | Conference Paper | en_US |
| refterms.dateFOA | 2018-08-16T17:06:33Z | |
| html.description.abstract | Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors. |
