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Lexical Acquisition for the Biomedical Domain

生物医学领域的词汇习得

基本信息

批准号：
EP/G051070/1
负责人：
Anna Korhonen
金额：
$ 36.37万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2009
资助国家：
英国
起止时间：
2009 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FG051070%2F1
关键词：
Lexical Acquisition Biomedical Domain

项目摘要

Natural Language Processing (NLP) is now critically needed to assist the processing, mining and extraction of knowledge from the rapidly growing literature in the area of biomedicine. In recent years, considerable progress has been made in the development of basic NLP techniques for biomedicine. The current challenge is to improve these techniques with richer and deeper analysis capable of supporting a wide range of real-world tasks. High-quality lexical resources (e.g. accurate and comprehensive lexicons and word classifications) are critically needed for this. Most lexical resources used in current systems are developed manually by linguists. Manual work is extremely costly, and the resulting resources require extensive labour-intensive porting to new (sub-)domains and tasks. Automatic acquisition or updating of lexical information from repositories of un-annotated text (e.g. corpora of biomedical articles) is a more promising avenue to pursue. Since lexical acquisition gathers usage and frequency information directly from relevant data, it can considerably enhance the viability and portability of NLP technology. Research into automatic lexical acquisition is now starting to produce large-scale resources useful for practical NLP tasks. However, the application of such techniques to biomedical texts has been limited because many existing techniques require adaptation before they can perform optimally in this linguistically challenging domain. In this project, we will take existing techniques capable of acquiring basic syntactic-semantic information for verbs from corpus data and will adapt them to the biomedical domain. We will focus on verbal (i) subcategorization frames, (ii) selectional preferences, and (ii) lexical-semantic classes. This information, when tailored to the domain in question, can aid key NLP tasks such as parsing, anaphora resolution, Information Extraction (IE), and question-answering (QA). Building on our pilot studies and expanding on the adaptive, state-of-the-art text processing tools available to us, we will improve existing techniques further and extend them with novel unsupervised and semi-supervised methods capable of supporting efficient domain adaptation. We will evaluate and demonstrate the capabilities of our techniques directly and in the context of practical BIO-NLP tasks. We will use the final version of the system to acquire a substantial lexical database from a biomedical corpus. The resulting resource will be distributed freely to the research community, along with the software which can be used to tune the frequency information stored in the database to particular biomedical sub-domains/tasks.We expect this project to (i) advance BIO-NLP and improve its usefulness for practical tasks in biomedicine, (ii) advance NLP by improving the accuracy, robustness and portability of lexical acquisition to real-world tasks, and (iii) provide an important large-scale study of domain-adaptation in the critical area of lexical acquisition.

现在迫切需要自然语言处理(NLP)来辅助处理、挖掘和提取生物医学领域中快速增长的文献中的知识。近年来，生物医学基础NLP技术的发展取得了长足的进步。当前的挑战是用更丰富和更深入的分析来改进这些技术，以便能够支持广泛的现实世界任务。为此，迫切需要高质量的词汇资源(如准确而全面的词典和单词分类)。当前系统中使用的大多数词汇资源都是由语言学家手动开发的。手工工作极其昂贵，由此产生的资源需要大量的劳动密集型移植到新的(子)域和任务。从未加注释的文本储存库(例如，生物医学文章语料库)自动获取或更新词汇信息是一种更有前途的途径。由于词汇习得直接从相关数据中收集使用情况和频率信息，因此可以极大地提高自然语言处理技术的可行性和可移植性。对词汇自动习得的研究现在已经开始产生对实际自然语言处理任务有用的大规模资源。然而，这些技术在生物医学文本中的应用一直是有限的，因为许多现有的技术需要适应，才能在这个具有语言学挑战性的领域取得最佳表现。在这个项目中，我们将采用现有的能够从语料库数据中获取动词的基本句法-语义信息的技术，并将它们应用于生物医学领域。我们将集中于动词(I)次范畴框架，(Ii)选择偏好，和(Ii)词汇-语义类别。当这些信息针对相关领域量身定做时，可以帮助执行关键的NLP任务，如解析、回指解析、信息提取(IE)和问答(QA)。在我们的初步研究和扩展现有的自适应、最先进的文本处理工具的基础上，我们将进一步改进现有的技术，并使用能够支持有效的领域适应的新的非监督和半监督方法来扩展它们。我们将直接评估和演示我们的技术能力，并在实际生物-NLP任务的背景下进行评估。我们将使用该系统的最终版本从生物医学语料库中获取大量的词汇数据库。我们期望这个项目能够(I)推进BIONAL-NLP并提高其在生物医学领域实际任务中的有用性，(Ii)通过提高词汇习得的准确性、稳健性和可移植性来促进NLP，以及(Iii)在词汇习得的关键领域中提供一项重要的大规模领域适应研究。