Computational natural language processing and the neuro-cognition of language

计算自然语言处理和语言的神经认知

• 批准号: EP/F030061/1
• 负责人: Lorraine Tyler
• 金额: $ 91.51万
• 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: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF030061%2F1
• 关键词: Computational; natural; language; processing; neuro

Scientific understanding of the human language system is one of the main challenges facing cognitive science, and of interest to fields as diverse as linguistics, psychology, anthropology, philosophy, biology and computer science. An adequate theory of this complex system would need to integrate scientific knowledge from several fields, many of which are still in a state of rapid development. One such field is cognitive neuroscience, which investigates language function in the human brain, with the aim of developing neurobiologically and cognitively plausible accounts of the human capacity for dynamic comprehension and production of language. A key input to this research is linguistic information about the core properties of language. This information is typically obtained from conventional resources (dictionaries and grammars) which provide useful generalisations about language, but which do not include statistical information about language use or capture the considerable variation that linguistic items undergo across time and age, data type, and genre. Such information would be an invaluable resource for neuro-cognitive experiments, increasing the plausibility of neurobiological models of language, but it can only be obtained by analysing linguistic patterns and their frequencies in the specific human language data (e.g. patient data, spoken language corpus) of experimental interest. Manual analysis of linguistic data is prohibitively expensive. Automated language analysis using computational Natural Language Processing (NLP) is now a viable alternative. The last decades have seen a massive expansion in the application of statistical and machine learning methods to NLP. This work has made large-scale processing of human language data possible and yielded impressive results in speech and language processing tasks, including e.g. speech recognition, morphological analysis, parsing, and semantic interpretation. Although the same methods could be used to provide realistic, data-driven linguistic input to neuro-cognitive studies involving language, there have been no systematic attempts to do this. The basic NLP technology is available, but it is inaccessible for researchers without considerable computing skills and requires further development for optimal integration with neuro-cognitive research. In this new interdisciplinary project we will integrate research in cognitive neuroscience, experimental psycholinguistics and NLP with the aim of providing the infrastructure for more realistic models of language structure for input into theoretically-driven empirical studies of language in the mind and brain. We will conduct a series of neuro-cognitive experiments which focus on the processing of the core components of language at the levels of morphology, syntax and semantics, using linguistic input automatically extracted from relevant human language data. NLP techniques will be improved and extended to deal with a wider range of constructions, domains and text types as required. An easy-to-use tool will then be designed which will enable effective search, extraction and summarisation of the linguistic information in the annotated data and optimal integration with neuro-cognitive experiments.We expect this project (i) to improve the quality of neuro-cognitive experiments by rooting them in a much more realistic linguistic analysis, (ii) to advance research in NLP by extending existing techniques enabling richer and deeper analysis, and (iii) to provide an important case study for the integration of NLP into critical experimental research in cognitive sciences. The long term goal of this investigation is improved scientific understanding of human language processing which can benefit several disciplines and place researchers in a better position to develop more useful language models, NLP technology, as well as treatments and rehabilitation of various language disorders in the future.

对人类语言系统的科学理解是认知科学面临的主要挑战之一，涉及语言学、心理学、人类学、哲学、生物学和计算机科学等多个领域。对这一复杂系统的充分理论需要整合来自几个领域的科学知识，其中许多领域仍处于快速发展状态。其中一个领域是认知神经科学，它研究人类大脑中的语言功能，目的是从神经生物学和认知角度对人类动态理解和产生语言的能力进行可信的解释。这项研究的一个关键输入是关于语言核心属性的语言信息。这种信息通常是从传统资源(词典和语法)获得的，这些资源提供了关于语言的有用概括，但不包括关于语言使用的统计信息，也不包括语言项目在时间和年龄、数据类型和体裁上经历的相当大的变化。这些信息将是神经认知实验的宝贵资源，增加了语言神经生物学模型的可信性，但它只能通过分析语言模式及其在实验感兴趣的特定人类语言数据(例如患者数据、口语语料库)中的频率来获得。人工分析语言数据的成本高得令人望而却步。使用计算自然语言处理(NLP)的自动语言分析现在是一种可行的替代方案。在过去的几十年里，统计和机器学习方法在自然语言处理中的应用得到了巨大的扩展。这项工作使大规模处理人类语言数据成为可能，并在语音和语言处理任务中取得了令人印象深刻的结果，包括语音识别、形态分析、句法分析和语义解释。虽然同样的方法可以用来为涉及语言的神经认知研究提供现实的、数据驱动的语言输入，但还没有系统地尝试这样做。基本的NLP技术是可用的，但对于没有相当计算技能的研究人员来说，它是不可用的，需要进一步开发，以实现与神经认知研究的最佳整合。在这个新的跨学科项目中，我们将整合认知神经科学、实验心理语言学和自然语言处理的研究，目的是为更现实的语言结构模型提供基础，以便输入到理论驱动的大脑和大脑语言的实证研究中。我们将进行一系列神经认知实验，利用从相关人类语言数据中自动提取的语言输入，在词法、句法和语义水平上处理语言的核心成分。自然语言处理技术将得到改进和扩展，以根据需要处理更广泛的结构、领域和文本类型。然后，我们将设计一个简单易用的工具，它将能够有效地搜索、提取和总结注释数据中的语言信息，并与神经认知实验进行最佳整合。我们希望这个项目(I)通过将神经认知实验植根于更现实的语言分析来提高神经认知实验的质量，(Ii)通过扩展现有技术来促进自然语言处理的研究，从而实现更丰富和更深入的分析，以及(Iii)为将自然语言处理整合到认知科学的关键实验研究中提供一个重要的案例研究。这项研究的长期目标是提高对人类语言处理的科学理解，这将使几个学科受益，并使研究人员处于更有利的地位，以开发更有用的语言模型、自然语言处理技术，以及未来各种语言障碍的治疗和康复。

项目成果

Parsing sentences are unlikely: corpus-based analyses of the neural processing of verbs

解析句子是不可能的：基于语料库的动词神经处理分析

• 作者: Barry Devereux (Author)

Improving Verb Clustering with Automatically Acquired Selectional Preferences

• DOI: 10.3115/1699571.1699596
• 发表时间: 2009-08
• 作者: Lin Sun;A. Korhonen

Lexical Isolation Point Software: a tool for predicting the lexical isolation point of a word

词汇隔离点软件：预测单词词汇隔离点的工具

• 作者: Andrew Thwaites (Co-Author)

Hierarchical Verb Clustering Using Graph Factorization

• 发表时间: 2011-07
• 作者: Lin Sun;A. Korhonen

Proceedings of the Interdisciplinary Workshop on Verbs - The Identification and Representation of Verb Features

动词跨学科研讨会论文集 - 动词特征的识别和表示

• 作者: Anna Korhonen (Editor)