RI: Small: CompCog: Modeling Latent Discrete Knowledge Across Utterances

每一种人类语言都是交流信息的惯例系统。然而，每个人是如何知道这个复杂系统的呢？即使是语言学家也很难描述它。然而，孩子们不知怎么就能掌握母语的规则和词汇。成年人在面对不熟悉的单词、与社交媒体相关的新惯例或新网站的布局惯例时，会继续学习。该项目为这类任务开发了新的人工智能方法。这些方法将使计算机能够处理更广泛的人类语言数据，从而改善信息获取和全球交流。他们还将提供关于为什么人类智能能够成功解决这些问题的见解。这些方法将寻求识别解释自然发生的语言数据模式的系统结构。具体来说，我们的计算机将分析自然发生的数据，以便学习：*如何将单词分解成有意义的部分，并将这些部分重新组合成新词。这是语言学家称之为语音学的主题。它在语音和文本的自动分析和翻译中具有重要的现实意义。如何把句子分解成有意义的短语。这需要确定语言的基本词序事实——语法归纳问题，被认为是人类语言学习的核心奥秘。*如何从以人类可读形式呈现数据库的大型网站中提取机器可读的数据。这包括自动计算出一个网站的数据库结构和布局惯例。如何在大量非正式文本中追踪人名。通过发现人们如何使用和修改名字的原则，计算机可以识别出昵称“弗拉德·p”或拼写错误的父名“弗拉基米尔·弗拉基米罗维奇”可能是“弗拉基米尔·普京”的变体，尤其是在政治评论中。该项目将以一种原则性的方式处理这些领域。我们在每个领域的策略是开发一种新的贝叶斯生成模型，以及用于近似推理的高效、有原则的机器学习算法。我们期望扩展自然语言处理社区可用的建模和推理技术的范围。创新的技术方向包括语音基础形式的自动重建，语法归纳作为结构化预测的新处理，数据库和数据库支持的网站的非参数模型，以及名称变化的系统发育模型。

Each human language is a system of conventions for communicating information. Yet how does everyone know this complex system? Describing it is difficult even for linguists. Yet young children somehow figure out the rules and vocabulary of their native language. Adults continue to learn when confronted with unfamiliar words, with new conventions associated with social media, or with the layout conventions of a new website. This project develops new artificial intelligence methods for tasks of this kind. These methods will enable computers to deal with a wider variety of human language data, thus improving information access and global communication. They will also provide insight as to why human intelligence is able to succeed at these problems.The methods will seek to discern the systematic structure that explains the patterns in naturally occurring linguistic data. Specifically, our computers will analyze naturally occurring data in order to learn:* How to break down words into meaningful parts and reassemble those parts into new words. This is a subject that linguists call morphophonology. It is practically important in automated analysis and translation of speech and text.* How to break down sentences into meaningful phrases. This requires determining the basic word order facts of the language -- the problem of grammar induction, considered to be a central mystery of human language learning.* How to extract machine-readable data from large websites that present databases in human-readable form. This involves automatically figuring out the database structure and layout conventions of a website.* How to track names across large quantities of informal text. By discovering the principles that govern how people use and modify names, a computer can recognize that the nickname "Vlad P." or the misspelled patronymic "Vladimir Vladimirovich" might be variant ways of referring to "Vladimir Putin," especially in a political comment. The project will address each of these domains in a principled way. Our strategy in each domain is to develop a novel Bayesian generative model along with efficient, principled machine learning algorithms for approximate inference. We expect to expand the range of modeling and inference techniques that are available to the natural language processing community. Innovative technical directions include the automatic reconstruction of phonological underlying forms, a novel treatment of grammar induction as structured prediction, a nonparametric model of databases and database-backed websites, and a phylogenetic model of name variation.