Advancing the Performance of Word Sense Disambiguation by Finding Consistent Criteria for Sense Distinctions

该项目的目标是提供基于面向信息处理需求的原则性英语义库的自动词义消歧系统。一词多义，即一个词的多种可能的解释或意义，是准确和集中信息处理的主要瓶颈之一。过去使用公共领域资源WordNet作为用于创建训练数据的词义清单的尝试没有成功，这是因为模糊和微妙的词义区别导致较差的注释者之间的一致性。我们正在试验将细粒度的WordNet词义分组为更粗粒度的词义区别的方法，以便更快、更准确地进行注释。使用语言学证据，我们正在改进我们对词义进行分组的方法和我们的注释过程，同时创建大量带有意义标记的文本。这个新的感觉清单链接到WordNet、FrameNet和VerbNet，具有与有助于准确的人类感觉标记的意义区分相关的明确标准。利用标注后的数据，通过实验不同的机器学习算法和特征集，我们正在开发准确的监督和半监督的自动词义消歧系统。SENSE清单、标记数据和训练的系统都将在公共领域内供国内和国际访问，提供面向计算应用的稳定的英语SENSE清单。广泛覆盖的自动词义消歧系统的可用将大大提高信息检索、信息提取、问答和机器翻译的性能，提高我们跟上信息雪崩的能力。

The goal of this project is to provide automatic word sense disambiguation systems based on a principled English sense inventory geared to information processing needs. Polysemy, the many possible interpretations, or senses, of a word, is one of the major bottlenecks to accurate and focused information processing. Past attempts to use a public domain resource, WordNet, as a sense inventory for creating training data have not been successful due to vague and subtle sense distinctions that lead to poor inter-annotator agreement. We are experimenting with approaches to group fine-grained WordNet senses into more coarse-grained sense distinctions that can be annotated more rapidly and more accurately. Using linguistic evidence, we are refining our methodology for grouping word senses and our annotation process while creating large amounts of sense-tagged text. This new sense inventory has links to WordNet, FrameNet, and VerbNet, with clear criteria associated with the sense distinctions that facilitate accurate human sense tagging. Using the annotated data we are developing accurate supervised and semi-supervised automatic word sense disambiguation systems by experimenting with different machine learning algorithms and feature sets. The sense inventory, the tagged data, and the trained systems will all be in the public domain for both national and international access, providing a stable English sense inventory geared to computational applications. The availability of broad coverage automatic word sense disambiguation systems will provide a major boost in performance to information retrieval, information extraction, question answering and machine translation, improving our ability to stay abreast of the information avalanche.