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

通过寻找语义区分的一致标准来提高词义消歧的性能

• 批准号: 0715078
• 负责人: Martha Palmer
• 金额: --
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-31 至 2009-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0715078&HistoricalAwards=false
• 关键词: Advancing; Performance; Word; Sense; Disambiguation

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.

本计画的目标是提供一套符合资讯处理需求的英文字义自动排歧系统。 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。 过去尝试使用公共域资源WordNet作为用于创建训练数据的感觉库存，但由于模糊和微妙的感觉区别导致注释者之间的协议不佳而没有成功。 我们正在尝试将细粒度的WordNet感觉分组为更粗粒度的感觉区别的方法，这些方法可以更快速，更准确地进行注释。 使用语言学证据，我们正在完善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的文本。 这个新的感官清单链接到WordNet，FrameNet和VerbNet，具有与感官区分相关的明确标准，有助于准确的人类感官标记。 使用注释数据，我们正在开发准确的监督和半监督自动词义消歧系统，通过试验不同的机器学习算法和特征集。 感官清单、标记数据和训练系统都将在公共领域供国内和国际访问，提供一个面向计算应用的稳定的英语感官清单。 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。