RI: Paraphrasing using Lexico-Semantic Resources

这个为期一年的概念验证试点项目使用公开可用的FrameNet语料库中编码的语义和语法以及来自公开可用的WordNet语料库的词汇语义信息生成语义标记输入句子的释义。首先使用现有的框架语义解析器（例如Shalmaneser）处理任意文本，然后将其传递给释义算法。该算法生成大量释义，这些释义与输入的语法和语义距离都很大。例如，给定输入“我喜欢吃奶酪”，系统输出语法上遥远的“我喜欢吃奶酪”，语义上遥远的“我害怕喝果汁”，以及成千上万个其他句子。生成的各种释义使该算法非常适合于一系列统计机器学习问题，如语言建模和机器翻译，以及其他依赖语义的任务，如查询和语言生成。该系统测试了人类对意译质量的判断，并通过测量在语言建模任务中使用意译的有效性。释义是从核威胁倡议语料库中生成的，该语料库已经用基本事实框架语义注释进行了手工标记。这允许分析：A.释义的质量与人类判断，B.相对准确性和数量与其他释义方法，C.使用自动框架解析与黄金标准注释的相对准确性和数量。

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.