RI: Small: Deep Natural Language Understanding with Probabilistic Logic and Distributional Similarity

网络提供了海量的信息，但这也使查找和提取相关信息变得更加困难。自然语言处理在开发提取信息和自动回答问题的工具方面取得了巨大进步，这些工具通常使用相对简单的方法，针对相对肤浅的分析。这个项目探索了更深入的分析和详细的自然语言理解的方法。现代智能系统长期以来一直使用逻辑来准确描述句子的含义以及它的各个部分是如何连接的。但这种精确度也有不利的一面：逻辑需要数据与其预期完全匹配，否则它就会崩溃。这对于像问答这样的应用程序来说是有问题的，因为语言是非常可变的。经常有很多不同的方式来表达相同的事情，或者说不完全相同但足够相似的事情，以使其相关。该项目将逻辑与一种技术相结合，该技术可以识别相似但不完全匹配的单词和段落。此外，语言往往只是暗示一些事情，而不是直截了当地陈述它们。该项目通过一种机制来处理这一问题，该机制得出可能但不是100%确定的结论，并说明其对结论的信心水平。作为高度跨学科的项目，该项目为学生提供了对逻辑和推理的见解，以及根据大量文本中的出现情况确定单词相似性的方法。该项目还通过双向传递思想，在计算语言学和理论语言学之间建立了新的联系。通过将精确和近似相结合，该项目为更深入地理解语言的语言技术铺平了道路，从而将加强信息提取和自动问题回答等具有重要社会意义的应用。自然语言语义中的任务需要越来越复杂和细粒度的推理。这个项目追求双重假设：(A)逻辑形式最适合支持这种推理，以及(B)有必要在词汇层面上对不确定的、概率的信息进行明确的推理。该项目将句子意义的逻辑形式表示与从分布相似性得出的加权推理规则相结合。它使用马尔可夫逻辑网络对带有加权规则的逻辑形式进行概率推理，测试识别文本蕴涵的任务。它还开发了在上下文中以一种服从于确定词汇蕴涵的方式来分布地描述词义的新方法。

The web offers huge amounts of information, but that also makes it harder to find and extract relevant information. Natural language processing has made huge strides in developing tools that extract information and automatically answer questions, often with relatively simple methods aimed at relatively superficial analysis. This project explores methods for a deeper analysis and detailed natural language understanding. Contemporary intelligent systems have long used logic to describe precisely what a sentence means and how its pieces connect. But this precision has a downside: Logic needs the data to exactly match its expectations, or it breaks down. This is problematic for applications like question answering because language is hugely variable. There are often many different ways to say the same thing, or to say things that are not exactly the same but similar enough to be relevant. This project combines logic with a technology that identifies words and passages that are similar but not exact matches. Also, language often only implies things rather than stating them outright. The project handles this through a mechanism that draws conclusions that are likely but not 100% certain, and that states its level of confidence in a conclusion. Being highly interdisciplinary, the project gives students insights into logic and inferences, as well as methods that determine word similarity based on occurrences in large amounts of text. This project also forges new links between computational and theoretical linguistics by transferring ideas in both directions. Through its combination of precision and approximation, this project paves the way for language technology that understands language more deeply and thus will enhance societally important applications such as information extraction and and automatic question answering. Tasks in natural language semantics are requiring increasingly complex and fine-grained inferences. This project pursues the dual hypotheses that (a) logical form is best suited for supporting such inferences, and that (b) it is necessary to reason explicitly about uncertain, probabilistic information at the lexical level. This project combines logical form representations of sentence meaning with weighted inference rules derived from distributional similarity. It uses Markov Logic Networks for probabilistic inference over logical form with weighted rules, testing on the task of Recognizing Textual Entailment. It also develops new methods for describing word meaning in context distributionally in a way that is amenable to determining lexical entailment.