用于基于置信度的化学命名实体识别的级联分类器

Cascaded classifiers for confidence-based chemical named entity recognition.

作者信息

Corbett Peter, Copestake Ann

机构信息

Unilever Centre For Molecular Science Informatics, Chemical Laboratory, University of Cambridge, CB21EW UK.

出版信息

BMC Bioinformatics. 2008 Nov 19;9 Suppl 11(Suppl 11):S4. doi: 10.1186/1471-2105-9-S11-S4.

DOI:10.1186/1471-2105-9-S11-S4

PMID:19025690

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2586753/

Abstract

BACKGROUND

Chemical named entities represent an important facet of biomedical text.

RESULTS

We have developed a system to use character-based n-grams, Maximum Entropy Markov Models and rescoring to recognise chemical names and other such entities, and to make confidence estimates for the extracted entities. An adjustable threshold allows the system to be tuned to high precision or high recall. At a threshold set for balanced precision and recall, we were able to extract named entities at an F score of 80.7% from chemistry papers and 83.2% from PubMed abstracts. Furthermore, we were able to achieve 57.6% and 60.3% recall at 95% precision, and 58.9% and 49.1% precision at 90% recall.

CONCLUSION

These results show that chemical named entities can be extracted with good performance, and that the properties of the extraction can be tuned to suit the demands of the task.

摘要

背景

化学命名实体是生物医学文本的一个重要方面。

结果

我们开发了一个系统，该系统使用基于字符的n元语法、最大熵马尔可夫模型和重打分来识别化学名称及其他此类实体，并对提取的实体进行可信度评估。一个可调整的阈值使系统能够调整到高精度或高召回率。在为平衡精度和召回率设置的阈值下，我们能够从化学论文中以80.7%的F值提取命名实体，从PubMed摘要中以83.2%的F值提取命名实体。此外，在95%的精度下，我们能够实现57.6%和60.3%的召回率，在90%的召回率下，我们能够实现58.9%和49.1%的精度。