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Accurate and Efficient Parsing of Biomedical Text

准确高效的生物医学文本解析

基本信息

批准号：
EP/E035698/1
负责人：
Stephen Clark
金额：
$ 26.89万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2007
资助国家：
英国
起止时间：
2007 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FE035698%2F1
关键词：
Accurate Efficient Parsing Biomedical Text

项目摘要

Natural Language Processing is a branch of Artificial Intelligence concerned with using computers to automatically process and understand natural languages. Natural language refers to languages such as English, French, German, etc., rather than artificial computer programming languages. There are a number of reasons why this will be an important technology in the 21st century. First, computers are gaining increasing importance in our society, and being able to communicate with them in a natural way, using spoken and written language, will become more desirable. Second, we are producing very large amounts of online electronic information; we require tools which can automatically process this information, to summarise it, to answer questions about it, to translate it, to find relevant documents within it. The staggering rise of Google demonstrates the importance of this kind of technology.The proposed research concerns the processing of a particular kind of text, namely the scientific articles produced by the biological research community. Biology produces an enormous number of new articles each year, far too many for any one individual to keep up to date with. Automatic computer tools are required which can process this information. For example, a biologist might want to know whether there is a paper on the Web answering a particular question about some gene.Sophisticated text processing, such as translating a document from one language to another, summarising documents, or answering questions, requires sophisticated language processing tools. A very useful tool for these kinds of tasks is a parser , which automatically determines the grammatical structure of a sentence and how the words in the sentence are related. For example, it would determine the verbs in the sentence, and how the nouns are related to the verbs. This information is needed if a computer is to be able to understand the text.The Natural Language Processing community now has very good parsing technology. However, the existing parsers are good at analysing certain kinds of text, such as newspapers, but not so good at other kinds of text, such as biology research papers. The reason is that the parsers have learned about language from linguistic resources created by humans, and the resources are based on newspaper text. Creating these resources from scratch for biology would take too long, and so the proposed research will investigate ways in which parsers tuned for newpaper text can be ported to handle biological text.

自然语言处理是人工智能的一个分支，它涉及使用计算机自动处理和理解自然语言。自然语言是指英语、法语、德语等语言，而不是人工计算机编程语言。这将成为21世纪世纪的一项重要技术，原因有很多。首先，计算机在我们的社会中越来越重要，能够使用口头和书面语言以自然的方式与它们交流将变得更加理想。第二，我们正在生产非常大量的在线电子信息;我们需要能够自动处理这些信息的工具，来总结它，回答有关它的问题，翻译它，在其中找到相关文档。Google的惊人崛起证明了这种技术的重要性。拟议的研究涉及处理特定类型的文本，也就是生物研究界发表的科学文章。生物学每年产生大量的新文章，任何一个人都无法跟上时代。需要自动计算机工具来处理这些信息。例如，生物学家可能想知道网上是否有一篇论文回答了关于某个基因的特定问题，复杂的文本处理，如将文档从一种语言翻译成另一种语言，总结文档或回答问题，需要复杂的语言处理工具。对于这类任务来说，一个非常有用的工具是解析器，它可以自动确定句子的语法结构以及句子中的单词是如何关联的。例如，它将确定句子中的动词，以及名词与动词的关系。如果计算机能够理解文本，这些信息是必需的。自然语言处理社区现在有非常好的解析技术。然而，现有的分析器只擅长分析某些类型的文本，如报纸，而不擅长分析其他类型的文本，如生物研究论文。原因是解析器从人类创造的语言资源中学习语言，而这些资源是基于报纸文本的。从零开始为生物学创建这些资源将花费太长时间，因此拟议的研究将调查为报纸文本调整的解析器可以移植到处理生物文本的方法。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Adapting a Lexicalized-Grammar Parser to Contrasting Domains

DOI：
10.3115/1613715.1613775
发表时间：
2008-10
期刊：
影响因子：
0
作者：
Laura Rimell;S. Clark
通讯作者：
Laura Rimell;S. Clark

Unbounded Dependency Recovery for Parser Evaluation

DOI：
10.3115/1699571.1699619
发表时间：
2009-08
期刊：
影响因子：
0
作者：
Laura Rimell;S. Clark;Mark Steedman
通讯作者：
Laura Rimell;S. Clark;Mark Steedman

Syntactic Processing Using the Generalized Perceptron and Beam Search

使用广义感知器和束搜索进行句法处理

DOI：
10.1162/coli_a_00037
发表时间：
2011
期刊：
Computational Linguistics
影响因子：
9.3
作者：
Zhang Y
通讯作者：
Zhang Y

Cambridge: Parser evaluation using textual entailment by grammatical relation comparison

剑桥：通过语法关系比较使用文本蕴涵进行解析器评估

DOI：
发表时间：
2010
期刊：
ACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings
影响因子：
0
作者：
Rimell L.
通讯作者：
Rimell L.

Faster Parsing by Supertagger Adaptation

DOI：
发表时间：
2010-07
期刊：
影响因子：
0
作者：
Jonathan K. Kummerfeld;Jessika Roesner;Tim Dawborn;J. Haggerty;J. Curran;S. Clark
通讯作者：
Jonathan K. Kummerfeld;Jessika Roesner;Tim Dawborn;J. Haggerty;J. Curran;S. Clark