@inproceedings{bcc34666ac5d4af2b2f670cbb6b18b90,
title = "Extracting conceptual terms from medical documents",
abstract = "Automated biomedical concept recognition is important for biomedical document retrieval and text mining research. In this paper, we describe a two-step concept extraction technique for documents in biomedical domain. Step one includes noun phrase extraction, which can automatically extract noun phrases from medical documents. Extracted noun phrases are used as concept term candidates which become inputs of next step. Step two includes keyphrase extraction, which can automatically identify important topical terms from candidate terms. Experiments were conducted to evaluate results of both steps. The experiment results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying document conceptual terms.",
keywords = "Conceptual term, Keyphrase extraction, Medical document, Noun phrase, Noun phrase extraction",
author = "Quanzhi Li and Wu, {Yi Fang Brook} and Xin Chen and Bot, {Razvan Stefan}",
year = "2005",
language = "English (US)",
isbn = "9781604235531",
series = "Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale",
pages = "2645--2651",
booktitle = "Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005",
note = "11th Americas Conference on Information Systems, AMCIS 2005 ; Conference date: 11-08-2005 Through 15-08-2005",
}