Constraint Solving for Language Processing and Bioinformatics

语言处理和生物信息学的约束求解

• 批准号: 2436-2012
• 负责人: Dahl, Veronica
• 金额: $ 1.02万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568245
• 关键词: Constraint; Solving; Language; Processing; Bioinformatics

In this proposal I address the problem of building executable theories of language intelligence. The grand goal in this area of Artificial intelligence (AI) is to automatically extract knowledge from human language expressions. This would enable knowledge encoded as natural language text to be combined with the power of AI reasoning mechanisms, which would facilitate a variety of AI applications, such as scientific discovery from web-based documents, within various disciplines. In particular, I propose to: (1) develop an appropriate constraint-based model(s) for parsing queries into knowledge and parsing potential "answers" (i.e., the subtexts that existing search engines return as possibly containing the answers) into knowledge bases from which inferences can be made to answer the query, so that a real answer is obtained instead of a URL; (2) incorporate correct ontological reasoning extensions with good complexity properties to remedy the problem that some of the subtexts proposed as answers in fact answer logically flawed interpretations of the query; and (3) demonstrate these methods in a prototype for molecular biology text mining that uses both English and DNA text as test corpora, paving the way to developing executable theories of language intelligence that could be applied to languages of molecular biology, as well as to human languages. My research will develop foundational and innovative interdisciplinary Web search methods and have immensely high practical and commercial potential: language understanding being the major bottleneck for search engine providers, any progress is likely to result in commercial opportunities and/or collaborations. Moreover, the envisaged DNA linguistics application has high social relevance and may be of great value to a large research community, e.g. allowing biologists to command computers directly through English, for tasks such as extracting genetic information of interest encoded in DNA strings, or gleaning relationships between biological entities mentioned in a large number of written documents, or simply obtaining a question's answer.

在这项建议中，我将讨论建立可执行的语言智能理论的问题。人工智能(AI)领域的宏伟目标是从人类语言表达中自动提取知识。这将使以自然语言文本编码的知识与人工智能推理机制的能力相结合，这将促进各种人工智能应用，例如在不同学科内从基于网络的文件进行科学发现。 具体地说，我建议：(1)开发一个适当的基于约束的模型(S)，用于将查询解析为知识，并将潜在的“答案”(即，现有搜索引擎返回的可能包含答案的子文本)解析到知识库中，从中可以做出推理来回答查询，从而获得真正的答案，而不是URL；(2)结合具有良好复杂性的正确的本体推理扩展，以弥补作为答案的一些子文本实际上回答的查询在逻辑上存在缺陷的问题；以及(3)在使用英语和DNA文本作为测试语料库的分子生物学文本挖掘原型中演示这些方法，为开发可应用于分子生物学语言和人类语言的可执行语言智能理论铺平道路。 我的研究将开发基础和创新的跨学科网络搜索方法，并具有极高的实用和商业潜力：语言理解是搜索引擎提供商的主要瓶颈，任何进展都可能带来商业机会和/或合作。此外，设想的DNA语言学应用具有很高的社会相关性，并可能对大型研究社区具有重要价值，例如允许生物学家直接通过英语指挥计算机，执行诸如提取DNA字符串中编码的感兴趣的遗传信息、收集大量书面文件中提到的生物实体之间的关系或简单地获得问题的答案等任务。