摘要翻译:
本文提出了几种新的词义消歧模型的有监督学习和无监督学习方法。有监督的方法集中于在概率模型空间中进行模型搜索,而无监督的方法依赖于使用吉布斯抽样和期望最大化(EM)算法。在有监督和无监督的情况下,朴素贝叶斯模型都表现良好。从学习速率和偏差-方差分解的角度对这一成功进行了解释。
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英文标题:
《Learning Probabilistic Models of Word Sense Disambiguation》
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作者:
Ted Pedersen
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最新提交年份:
2007
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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 dissertation presents several new methods of supervised and unsupervised learning of word sense disambiguation models. The supervised methods focus on performing model searches through a space of probabilistic models, and the unsupervised methods rely on the use of Gibbs Sampling and the Expectation Maximization (EM) algorithm. In both the supervised and unsupervised case, the Naive Bayesian model is found to perform well. An explanation for this success is presented in terms of learning rates and bias-variance decompositions.
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PDF链接:
https://arxiv.org/pdf/0707.3972