中文摘要：

全词消歧（All-Words Word Sense Disambiguation）可以看作一个序列标注问题,该文提出了两种基于序列标注的全词消歧方法,它们分别基于隐马尔可夫模型（Hidden Markov Model,HMM）和最大熵马尔可夫模型（Maximum Entropy Markov Model,MEMM）。首先,我们用HMM对全词消歧进行建模。然后,针对HMM只能利用词形观察值的缺点,我们将上述HMM模型推广为MEMM模型,将大量上下文特征集成到模型中。对于全词消歧这类超大状态问题,在HMM和MEMM模型中均存在数据稀疏和时间复杂度过高的问题,我们通过柱状搜索Viterbi算法和平滑策略来解决。最后,我们在Senseval-2和Senseval-3的数据集上进行了评测,该文提出的MEMM方法的F1值为0.654,超过了该评测上所有的基于序列标注的方法。

英文摘要：

All-Words Word Sense Disambiguation（WSD） can be regarded as a sequence labeling problem,and two All-Words WSD methods based on sequence labeling are proposed in this paper,which are based on Hidden Markov Model（HMM） and Maximum Entropy Markov Model（MEMM）,respectively.First,we model All-Words WSD using HMM.Since HMM can only exploit lexical observation,we generalize HMM to MEMM by incorporating a large number of non-independent features.For All-Words WSD which is a typical extra-large state problem,the data sparsity and high time complexity seriously hinder the application of HMM and MEMM models.We solve these problems by beam-search Viterbi algorithm and smoothing strategy.Finally,we test our methods on the dataset of All-Words WSD tasks in Senseval-2 and Senseval-3,and achieving a 0.654 F1 value forthe MEMM method which outperforms other methods based on sequence labeling.