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

RI：小：利用概率逻辑和分布相似性进行深度自然语言理解

• 批准号: 1523637
• 负责人: Katrin Erk
• 金额: $ 40.83万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523637&HistoricalAwards=false
• 关键词: RI; Small; Deep; Natural; Language

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.

Web提供了大量信息，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工具方面取得了长足的进步，这些工具通常采用相对简单的方法来提取信息并自动回答问题。该项目探讨了更深入分析和详细自然语言理解的方法。当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其作品的连接方式。但是，此精度有一个缺点：逻辑需要数据以与其期望完全匹配，否则它会崩溃。对于诸如问答响应之类的应用程序，这是有问题的，因为语言是大量可变的。通常有许多不同的方法可以说同一件事，或者说不完全相同但相似以至于相关的话。该项目结合了逻辑与一项识别类似但不是完全匹配的单词和段落的技术。同样，语言通常仅意味着事物而不是直接说明它们。该项目通过得出可能但不能确定的结论的机制来处理这一点，并指出了其对结论的信心水平。该项目是高度跨学科的，使学生对逻辑和推论的洞察力以及根据大量文本的出现来确定单词相似性的方法。该项目还通过在两个方向上转移思想来建立计算和理论语言学之间的新联系。通过其精确度和近似结合，该项目为语言技术铺平了更深入地理解语言的语言，从而增强了社会上重要的应用程序，例如信息提取和自动问题答案。自然语言语义的任务需要日益复杂且细粒度的推论。该项目提出了双重假设：（a）逻辑形式最适合支持这种推论，并且（b）有必要明确推论词汇层面上不确定的概率信息。该项目将句子含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Markov逻辑网络对逻辑形式进行了概率推断，并使用加权规则进行了测试，以识别文本需要的任务。它还开发了新的方法，以在上下文分布中以一种可以确定词汇的方式来描述单词含义。