ChemicalTagger: A tool for semantic text-mining in chemistry.

ChemicalTagger: A tool for semantic text-mining in chemistry.
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DOI:
10.1186/1758-2946-3-17
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发表时间:
2011-05-16
影响因子:
8.6
通讯作者:
Murray-Rust P
Murray-Rust P
中科院分区:
化学2区
文献类型:
--
作者:
Hawizy L;Jessop DM;Adams N;Murray-Rust P

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科学交流的主要方法是使用自然语言与域特异性术语结合的发表的科学文章和论文的形式。因此,它们包含免费的欠非结构化文本。鉴于从非结构化文献中提取数据的有用性,我们旨在展示如何实现化学学科。大多数化学家采用的高度配方式写作风格使他们的贡献非常适合高通量自然语言处理(NLP)方法。 我们已经开发了ChemicalTagger解析器作为化学实验语言的中等深度,基于短语的语义NLP工具。标记基于模块化体系结构,并结合了奥斯卡,域特异性正则和英语标记器的组合来识别词性的部分。 ANTLR语法用于将其构造为基于树的短语。使用允许重叠注释的度量,我们实现了短语识别的机器通道协议为88.9%,而短语类型识别(动作名称)则达到了91.9%。 使用基于规则的技术与形式的语法解析器结合使用基于规则的技术,可以分析化学实验文本。 ChemicalTagger已被部署为10,000多种专利,并从语言环境中鉴定出溶剂的精度> 99.5%。
The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.
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影响因子: 3.2
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期刊: BMC bioinformatics
影响因子: 3
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DOI: 10.1021/ci100384d
发表时间: 2011-03-01
影响因子: 5.6
作者:
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通讯作者: Glen, Robert C.