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

这个项目的目标是提供自动的词义消歧系统，该系统基于一个原则的英语词义清单，以适应信息处理的需要。一词多义，即一个词的多种可能的解释或意思，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作为创建训练数据的感觉清单的尝试并没有成功，因为模糊和微妙的感觉差异导致注释者之间的一致性很差。我们正在试验一些方法，将细粒度的WordNet感觉分组为更粗粒度的感觉区分，这样可以更快、更准确地进行注释。使用语言学证据，我们正在改进我们的方法来分组词义和我们的注释过程，同时创建大量的意义标记文本。这个新的语义清单与WordNet、FrameNet和vernet有链接，具有与语义区分相关的明确标准，有助于准确地进行人类语义标记。利用标注数据，我们通过实验不同的机器学习算法和特征集，开发了精确的监督和半监督自动词义消歧系统。感测目录、标记数据和训练系统都将在公共领域，供国内和国际访问，为计算应用提供稳定的英语感测目录。广泛覆盖的自动词义消歧系统的可用性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.