ITR/SY(CISE) Learning Syntactic/Semantic Information for Parsing

这项研究关注的是对英语结构信息的无监督学习，这些信息在当前的树库（特别是各种Penn树库）中不存在。 也就是说，人们希望机器能够学习这些信息，而不必创建注释信息的语料库。 要学习的结构信息通常福尔斯在语法和语义之间的边界上;例如，“纽约证券交易所”的名称中有“纽约”这个位置，这一事实属于语法还是语义？ 那么，“推销无用的东西”和“无用的东西的市场”这两种表达方式之间的相似性是什么呢？ 目的是以当前统计解析器可以使用的形式学习这种信息，以便它们可以输出更精细结构的解析。 但这并不意味着解析是这类信息的唯一用途。 越来越多的用于从自由文本中自动提取信息的系统使用共指检测和“命名实体识别”（例如，认识到“纽约”是一个地点，但“纽约证券交易所”是一个组织）。 有证据表明，共指和命名实体的识别可以提高更精细的分析水平，使这项研究成为可能。 再一次，“语言模型”（为语言中的字符串分配概率的程序）是所有当前语音识别系统的标准部分;有证据表明，更细粒度的句法分析可以改进当前的语言模型。 因此，这项研究将使各种各样的系统，以更好地利用语言输入，使这些系统更容易访问不同的用户池。

This research concerns the unsupervised learning of structural information about English that is not present in current tree-banks (specifically the various Penn tree-banks). That is, one wants a machine to learn this information without having to create a corpus in which the information is annotated. The structural information to be learned often falls at the boundary between syntax and semantics; for example, does the fact that the "New York Stock Exchange" has as part of the name the location "New York" fall under syntax or semantics? What about the similarity between the expressions "[to] market useless items" and "the market for useless items"? The intention is to learn this kind of information in a form that current statistical parsers can use so that they can output more finely structured parses. But this is not meant to suggest that parsing is the sole use for this sort of information. More and more systems for automatically extracting information from free text use coreference detection and "named-entity recognition" (e.g., recognizing that "New York" is a location, but "New York Stock Exchange" is an organization). There is evidence to suggest that both coreference and named-entity recognition can be improved with the finer level of analysis to be made possible by this research. Or again, "language models" (programs that assign a probability to strings in a language) are standard parts of all current speech-recognition systems; there is evidence that suggests that finer grained syntactic analysis can improve current language models. Thus, this research will enable a wide variety of systems to make better use of language input and so make these systems more accessible to a diverse user pool.