摘要翻译:
本文描述了一个混合系统,针对英语全单词和词汇样本任务,它依赖于两种不同的无监督方法。第一种方法根据上下文词之间的互信息接近度选择词义,即词义的变体。第二个启发式分析了词义注释中的使用实例,从而推断出简单的句法模式。这些模式与消歧上下文进行匹配。结果表明,第一个启发式在全词任务中的查准率和查全率分别为0.58和0.35,而第二个启发式分别为0.80和0.25。所获得的高精度建议对该技术进行更深入的研究。还提供了词汇示例任务的结果。
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英文标题:
《Word Sense Disambiguation Based on Mutual Information and Syntactic
Patterns》
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作者:
David Fernandez-Amoros
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最新提交年份:
2009
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Computation and Language 计算与语言
分类描述:Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意,人工语言(编程语言、逻辑学、形式系统)的工作,如果没有明确地解决广义的自然语言问题(自然语言处理、计算语言学、语音、文本检索等),就不适合这个领域。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
This paper describes a hybrid system for WSD, presented to the English all-words and lexical-sample tasks, that relies on two different unsupervised approaches. The first one selects the senses according to mutual information proximity between a context word a variant of the sense. The second heuristic analyzes the examples of use in the glosses of the senses so that simple syntactic patterns are inferred. This patterns are matched against the disambiguation contexts. We show that the first heuristic obtains a precision and recall of .58 and .35 respectively in the all words task while the second obtains .80 and .25. The high precision obtained recommends deeper research of the techniques. Results for the lexical sample task are also provided.
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PDF链接:
https://arxiv.org/pdf/0910.5419