本文提出了一种基于小规模语料库和机器可读词典(Machine Readable Dictionary ,MRD)的无指导的动词语义获取方法。该方法不需要使用有义项标注的语料库,而是使用从语料中获得的V+N搭配以及MRD中多义词定义的应用实例中获得的知识。使用两种方法解决数据稀疏问题:首先,将词的相似性度量由直接共现扩展到共现词的共现,以共现聚类而不是共现词来计算词的相似度。其次,从MRD定义中获取名词的IS- A关系。通过这些方法,即使两个词不共享任何词,也可认为是相似的。实验表明,该方法可从很小规模的语料中获取知识,并在不限制词义的情况下达到85.7%的正确排歧率。

This paper presents a systemfor unsupervised verb semantic knowledge acquisition using small corpus and a machine-readable dictionary (MRD) . The system does not depend on sense-tagged corpus , but learns a set of typical usages listed in the MRD usage examples for each of the senses of a polysemous verb in the MRD definitions and uses verb-object co-occurrences acquired from the corpus. This paper concentrates on the problemof data sparseness in two ways. First , extending word similarity measures from direct co-occurrences to co-occurrences of co-occurred words , we compute the word similarities using not co-occurred words but co-occurred clusters. Second , we acquire IS-A relations of nouns from the MRD definitions. It is possible to cluster the nouns roughly by the identification of the IS-A relationship. By these methods , two words may be considered similar even if they do not share any word. Experiments show that this method can learn from very small training corpus and achieve over 85.7% correct disambiguation performance without a restriction of word’s senses.