CAREER: Resolving Lexical Ambiguities in Natural Language Processing

职业：解决自然语言处理中的词汇歧义

• 批准号: 9985033
• 负责人: David Yarowsky
• 金额: $ 33万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9985033&HistoricalAwards=false
• 关键词: CAREER; Resolving; Lexical; Ambiguities; Natural

This is the first year of funding of a 4 year, continuing award. One of the major roadblocks in theefficient and accurate communication between humans and machines is the resolution of theambiguity inherent in natural languages. A major bottleneck in developing solutions is the severeshortage of training data that distinguishes word senses, and the high cost of inputting thisinformation manually. A focus of this project is the development of unsupervised and minimallysupervised algorithms for acquiring such skills without costly hand-tagged training data. Suchmethods will exploit the distribution properties observed in very large text corpora (over 10 billionwords); the PI will also investigate richer representations of feature space, class models,smoothing methods and learning algorithms specialized for classification in very high-dimensionalfeaturespaces. In addition to the problem of word-sense disambiguation, this project will exploreshared solutions to a closely related set of lexical-ambiguity tasks including spelling correction,propername classification, capitalization restoration, accent and diacritic restoration for, diverselanguages, vowel restoration in Hebrew and Arabic, speech synthesis on homographs, lexicalchoice in machine translation, and certain aspects of choosing among phonetically confusablecandidates in speech recognition. These diverse problems are not normally recognized as beingmembers of the same class, and this project seeks to exploit the synergies present by developingmethods and training data on one member of the class and utilizing the methods and data on other.problems in the class. Thus this unified approach offers the potential for rapid parallel progress onkey problems in human-computer interaction and information extraction.

这是一个为期4年的连续奖励的第一年。人类和机器之间高效准确沟通的主要障碍之一是解决自然语言中固有的歧义。开发解决方案的一个主要瓶颈是区分词义的训练数据的短缺，以及手动输入这些信息的高昂成本。该项目的重点是开发无监督和最小监督算法，用于在没有昂贵的手工标记训练数据的情况下获得这些技能。这些方法将利用在非常大的文本语料库（超过100亿字）中观察到的分布特性; PI还将研究更丰富的特征空间表示、类模型、平滑方法和专门用于非常高维特征空间分类的学习算法。除了词义消歧的问题之外，该项目还将探索一系列密切相关的词汇歧义任务的共享解决方案，包括拼写纠正，专有名称分类，大写恢复，口音和变音符号恢复，多种语言，希伯来语和阿拉伯语的元音恢复，同形异义词的语音合成，机器翻译中的词汇选择，以及在语音识别中从语音易混淆的候选者中进行选择的某些方面。这些不同的问题通常不被认为是同一类的成员，这个项目试图通过开发类中一个成员的方法和训练数据，并利用类中其他问题的方法和数据来利用协同效应。因此，这种统一的方法提供了潜在的快速并行进展的关键问题，在人机交互和信息提取。