STATGEN: Robust, Scalable Language Generation Using Symbolic and Statistical Techniques

摘要：凯文·奈特南加州大学143499 - 12美元。使用符号和统计技术生成健壮的、可扩展的语言。自然语言处理包括语言分析（文本输入）和语言生成（文本输出）。虽然许多应用程序都需要这两种功能，但到目前为止，大部分研究和开发都是基于前者。因此，许多实际系统（如翻译、解释、对话）中的弱点可以追溯到自然语言生成（NLG）中的经典问题。新的统计技术使得通过自动从在线文本语料库中提取知识来解决经典问题成为可能，但到目前为止，这些技术主要应用于语言分析，而不是NLG。例如，词义消歧（从词到概念）是近年来研究的热点，而词汇选择（从概念到词）相对较少。句子解析（分析）和句子构造（生成）之间也存在类似的差异。虽然可训练的解析器现在可以在不受限制的文本上操作，但NLG通常需要完美的输入，并依赖于手工制作的特定于领域的知识。我们相信，统计方法有潜力在短期内改善NLG技术，实现新的应用，并开辟新的研究问题。我们的研究将强调准确性、可扩展性、稳健性和评估；它将结合手工构建的语法、在线词汇资源和新颖的“阅读学习”方法，从在线文本中自动收集知识。

AbstractIIS-9820291Kevin KnightUniversity of Southern California$143,499 - 12 mos.Robust, Scalable Language Generation Using Symbolic and Statistical Techniques This is a three-year continuing award. Natural language processing comprises both language analysis (text in) and language generation (text out). While many applications need both capabilities, the bulk of research and development has so far been on the former. As a result, weaknesses in many practical systems (e.g., translation, explanation, dialogue) are traceable to classic problems in natural language generation (NLG). New statistical techniques have made it possible to address classic problems through the extraction of knowledge automatically from online text corpora, but so far these techniques have been applied primarily to language analysis, and not to NLG. For example, word-sense disambiguation (word to concept) has been the object of intense recent study, while lexical selection (concept to word) has languished, relatively speaking. A similar discrepancy exists between sentence parsing (analysis) and sentence structuring (generation). While trainable parsers can now operate on unrestricted text, NLG usually requires perfect inputs and relies on handcrafted, domain-specific knowledge. We believe that statistical methods have the potential to improve NLG technology in the near term, to enable new applications, and to open up new research problems. Our research will emphasize accuracy, scalability, robustness, and evaluation; it will combine hand-built grammar, online lexical resources, and novel "learning by reading" approaches for gathering knowledge automatically from online texts.