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

网络提供了大量的信息，但这也使得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，通常使用相对简单的方法进行相对肤浅的分析。该项目探索了更深入的分析和详细的自然语言理解方法。当代智能系统长期以来一直使用逻辑来精确描述句子的含义以及句子的各个部分之间的联系。但这种精确度有一个缺点：逻辑需要数据完全符合它的预期，否则它就会崩溃。这对于问答之类的应用程序来说是有问题的，因为语言是非常多变的。同一件事通常有很多不同的表达方式，或者说一些不完全相同但又足够相似的事情。这个项目结合了逻辑和一种技术，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，而不是直接陈述。项目通过一种机制来处理这个问题，该机制可以得出可能但不是100%确定的结论，并说明其对结论的信心程度。作为高度跨学科的项目，该项目让学生深入了解逻辑和推理，以及根据大量文本中的出现情况确定单词相似度的方法。该项目还通过在两个方向上传递思想，在计算语言学和理论语言学之间建立了新的联系。该项目将精确性和近似性相结合，为深入理解语言的语言技术铺平了道路，从而增强了信息提取和自动问答等社会重要应用。自然语言语义中的任务需要越来越复杂和细粒度的推理。这个项目追求两个假设：(a)逻辑形式最适合支持这样的推理，(b)有必要在词汇水平上对不确定的概率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度派生的加权推理规则相结合。利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，对文本蕴涵识别任务进行测试。它还发展了新的方法来描述词的意义分布在上下文中，以一种方式，是服从于确定词汇蕴涵。

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.