模糊逻辑在词语搭配抽取中的应用
The Application of Fuzzy Logic to Collocation Extraction
摘要：
搭配对于自然语言处理的许多任务都是非常重要的，如信息检索、机器翻译、计算词典编纂等。几乎所有的方法都形成了一个经典的简洁搭配集。提出了一种模糊逻辑的搭配抽取方法，形成一个模糊搭配集，其中每个词组合都有一定的搭配隶属度。模糊逻辑提供了一种将自然语言表达为模糊逻辑规则的简单方法。两个现有的方法;互信息和t-测试已被用于模糊推理系统的输入。由此产生的隶属函数可以很容易地看到和演示。为了说明模糊逻辑的实用性，我们以某些词对为例进行了分析。的工作数据已基于语料库的约100万字，包含在不同的小说，构成项目古腾堡可在www.gutenberg. org。所提出的方法具有这两种方法的所有优点，同时克服了它们的缺点。因此，它提供了比这两种方法更好的结果。
英文摘要：
Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost all the methods form a classical crisp set of collocation. We propose a fuzzy logic approach of collocation extraction to form a fuzzy set of collocations in which each word combination has a certain grade of membership for being collocation. Fuzzy logic provides an easy way to express natural language into fuzzy logic rules. Two existing methods; Mutual information and t-test have been utilized for the input of the fuzzy inference system. The resulting membership function could be easily seen and demonstrated. To show the utility of the fuzzy logic some word pairs have been examined as an example. The working data has been based on a corpus of about one million words contained in different novels constituting project Gutenberg available on www.gutenberg.org. The proposed method has all the advantages of the two methods, while overcoming their drawbacks. Hence it provides a better result than the two methods.
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