SGER: Exploratory Research of Word Sense Disambiguation Methods for All Words in Open Text

词义消歧（WSD）是自然语言处理中的一项核心任务，对于文本理解、常识推理和机器翻译等主要应用程序至关重要。以前对WSD的研究已经为可获得训练数据的相对较少的单词制定了很好的消歧方案。相比之下，很少有人尝试创建能够消除开放文本中所有单词歧义的系统。这个为期一年的计划的目标是对各种WSD技术进行探索性研究，以便开发一种工具，为开放文本中的所有单词进行语义标记。要研究的方法依赖于词义的内部和外部表征。内在表征来自于词义的例子；而外部表征则由词义之间的语义关系给出。这两种表示对应于关于词义的两种不同观点，这两种观点可用于派生互补的WSD技术，这些技术最终可以组合在一起，产生解决开放文本中所有单词的语义歧义的工具。作为该项目的一部分所开发的技术有望显著提高WSD开放文本应用程序的性能水平，并对自然语言处理中的其他重要应用程序产生影响。

Word Sense Disambiguation (WSD) is a core task in natural language processing and is considered essential for major applications like text understanding, common sense reasoning, and machine translation. Previous research on WSD has produced good disambiguation schemes for the relatively few words for which training data has been available. In contrast, there have been few attempts to create systems that disambiguate all words in open text. The goal of this one-year project is to conduct exploratory research of various WSD techniques to enable the development of a tool for semantic tagging of all words in open text.The methods to be investigated rely on inner and outer representations of word sense. The inner representation comes from examples of word meanings; whereas, the outer representation is given by semantic relations between word senses. These two representations correspond to two different views on word meanings that can be used to derive complementary WSD techniques, which ultimately can be combined, yielding a tool for resolving the semantic ambiguity of all words in open text.The techniques developed as part of this project are expected to significantly improve the performance level of WSD applications for open text and should have an impact on other important applications in natural language processing.