Constraint Solving for Language Processing and Bioinformatics

在这个建议中，我解决的问题，建立可执行的语言智能理论。人工智能（AI）这一领域的宏伟目标是从人类语言表达中自动提取知识。这将使编码为自然语言文本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，例如在各种学科中从基于网络的文档中进行科学发现。 特别地，我建议：（1）开发一个适当的基于约束的模型，用于将查询解析为知识并解析潜在的“答案”（即，现有搜索引擎返回的可能包含答案的潜文本）到知识库中，从知识库中可以进行推理以回答查询，从而获得真实的答案而不是URL;（2）结合具有良好复杂性属性的正确本体论推理扩展，以补救作为答案提出的某些潜文本实际上回答了对查询的逻辑上有缺陷的解释的问题;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以及人类语言的语言智能的可执行理论铺平了道路。 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，任何进展都可能导致商业机会和/或合作。此外，设想的DNA语言学应用具有高度的社会相关性，并且可能对大型研究社区具有很大价值，例如允许生物学家直接通过英语命令计算机，用于诸如提取编码在DNA字符串中的感兴趣的遗传信息的任务，或者收集大量书面文档中提到的生物实体之间的关系，或者简单地获得问题的答案。

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.