使用隐马尔可夫模型和点互信息的无词典和无上下文的药品名称识别方法

Jacek Małyszko, A. Filipowska
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引用次数: 3

摘要

本文关注从波兰文自由文本文档中提取药品名称的问题。使用基于词典的方法,不可能识别未知或拼写错误的药物名称。本文介绍了两种方法的实验结果:隐马尔可夫模型(HMM)和基于点互信息(PMI)的方法。实验是在不使用词汇或上下文信息的情况下识别药物名称。实验结果表明,隐马尔可夫可以作为药品名称识别的几个步骤之一(测试集的f分数略低于70%),而PMI可以帮助提高使用隐马尔可夫获得的结果的精度,但在召回率上有显著损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lexicon-free and context-free drug names identification methods using hidden markov models and pointwise mutual information
The paper concerns the issue of extraction of medicine names from free text documents written in Polish. Using lexicon-based approaches, it is impossible to identify unknown or misspelled medicine names. In this paper, we present the results of experimentation on two methods: Hidden Markov Model (HMM) and Pointwise Mutual Information (PMI)-based approach. The experiment was to identify the medicine names without the use of lexicon or contextual information. The experimentation results show, that HMM may be used as one of several steps in drug names' identification (with F-score slightly below 70% for the test set), while the PMI can help in increasing the precision of results achieved using HMM, but with significant loss in recall.
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