RI: Paraphrasing using Lexico-Semantic Resources

RI：使用词汇语义资源进行释义

• 批准号: 0713627
• 负责人: Adam Janin
• 金额: $ 10万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713627&HistoricalAwards=false
• 关键词: RI; Paraphrasing; using; Lexico; Semantic

This one-year pilot proof-of-concenpt project generates paraphrases of semantically labeled input sentences using the semantics and syntax encoded in the publicly available FrameNet corpus and the lexical semantic information from the publicly available WordNet corpus.Arbitrary text is first processed using an existing frame-semantic parser (such as Shalmaneser), and then passed to the paraphrasing algorithm. The algorithm generates a large number of paraphrases with a wide range of syntactic and semantic distances from the input.For example, given the input ``I like eating cheese'', the system outputs the syntactically distant ``Eating cheese is liked by me'', the semantically distant ``I fear sipping juice'', and thousands of other sentences. The large variety of generated paraphrases makes the algorithm ideal for a range of statistical machine learning problems such as language modeling and machine translation, as well as other semantics-dependent tasks such as query and language generation.The system is tested both against human judgments of paraphrase quality and by measuring the efficacy of using paraphrases in a language modeling task. Paraphrases are generated from the Nuclear Threat Initiative corpus, which has been hand-marked with ground-truth frame-semantic annotations. This allows an analysis of: A. the quality of the paraphrases vs. human judgments, B. the relative accuracy and quantity vs. other paraphrasing methods, and C. the relative accuracy and quantity of using automatic frame parsing vs. gold-standard annotations.

这个为期一年的概念验证试验项目使用公共可用的FrameNet语料库中编码的语义和语法以及公共可用的WordNet语料库中的词汇语义信息来生成语义标记的输入句子的释义。任意文本首先使用现有的框架语义解析器（如Shalmaneser）进行处理，然后传递给释义算法。该算法生成了大量的释义，这些释义与输入之间具有广泛的句法和语义距离。例如，给定输入"我喜欢吃奶酪“，系统会输出句法距离较远的”我喜欢吃奶酪“、语义距离较远的"我害怕喝果汁”以及数千个其他句子。生成的大量释义使该算法成为一系列统计机器学习问题的理想选择，如语言建模和机器翻译，以及其他语义相关的任务，如查询和语言生成。该系统既针对人类对释义质量的判断进行了测试，也通过测量在语言建模任务中使用释义的功效。释义是从核威胁倡议语料库，这已被手工标记与地面真相框架语义注释。这允许分析：A.释义的质量与人类的判断，B。与其他释义方法相比，C.使用自动帧解析与黄金标准注释的相对准确性和数量。