Adding Domain Knowledge to Inductive Learning Methods for Classifying Texts

将领域知识添加到归纳学习方法中以对文本进行分类

• 批准号: 9619713
• 负责人: Kevin Ashley
• 金额: $ 16.28万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9619713&HistoricalAwards=false
• 关键词: Adding; Domain; Knowledge; Inductive; Learning

The research focuses on improving current methods for learning to classify texts by incorporating knowledge from an expert domain model. The goal is automatically to classify the texts of legal opinions in terms of the factors that apply to the cases described. Factors -- stereotypical fact patterns tending to strengthen or weaken the underlying legal claims in a case, and their relations to legal issues -- are a kind of expert domain knowledge useful in legal argumentation. The program takes as inputs the raw texts of legal opinions and assigns as outputs the applicable factors. The program's training instances are drawn from a corpus of legal opinions whose textual descriptions of cases have been represented manually in terms of factors. The problem is hard because the language of the opinions is complex; the mere fact that an opinion discusses factors does not necessarily imply that those factors actually apply to the case. Starting with several existing inductive and statistical learning algorithms, this research assesses whether adding four different kinds of domain knowledge improves the algorithms' performance: (1) domain knowledge about factors and the legal issues to which they relate; (2) general information about the structure of legal opinions; (3) information about the statutes quoted in an opinion; (4) information about those cases cited in an opinion whose factors are known. The work also explores (a) how to combine inductive and analytical techniques to deal with small numbers of training instances and (b) how best to combine successful inductive, statistical, and knowledge-based methods. Using domain knowledge to guide automatic text classification integrates information retrieval, machine learning and AI knowledge-representation techniques, will help scale up case-based reasoning systems, and alleviate the problem of assessing the relevance of texts in increasingly large on-line databases.

该研究的重点是改进现有的方法，学习分类文本，将知识从专家领域模型。其目标是根据适用于所述案件的因素自动对法律的意见文本进行分类。因素--倾向于加强或削弱案件中潜在的法律的主张及其与法律的问题的关系的刻板事实模式--是一种在法律的论证中有用的专家领域知识。该程序将法律的意见的原始文本作为输入，并将适用的因素作为输出。该程序的训练实例是从一个语料库的法律的意见，其文本描述的情况下，已手动表示的因素。这个问题很难解决，因为意见的语言很复杂;一项意见讨论各种因素这一事实本身并不一定意味着这些因素实际上适用于该案件。本研究从几种现有的归纳和统计学习算法开始，评估了添加四种不同类型的领域知识是否会提高算法的性能：（1）关于因素及其相关的法律的问题的领域知识;（2）关于法律的意见结构的一般信息;（3）关于意见中引用的法规的信息;（四）意见中引用的因素已知的案件的情况。这项工作还探讨了（a）如何将联合收割机归纳和分析技术相结合，以处理少量的训练实例，以及（B）如何最好地将联合收割机成功的归纳，统计和基于知识的方法相结合。利用领域知识指导文本自动分类，集成了信息检索、机器学习和人工智能知识表示技术，将有助于扩大基于案例的推理系统，并缓解在日益庞大的在线数据库中评估文本相关性的问题。