Abstract
Word sense disambiguation (WSD) is one of tricky tasks in natural language processing (NLP) as it needs to take into full account all the complexities of language. Because WSD involves in discovering semantic structures from unstructured text, automatic knowledge acquisition of word sense is profo
Highlights
Automatic acquisition of knowledge is by far one of mostly used technologies in natural language processing (NLP)
This reasoning pattern supposes that reasoning of knowledge is more a process of similarity comparison based on experience than a process of condition-action based on conception induction
We have performed experiment with the method proposed in this paper on corpora of 5 million characters
Summary
Automatic acquisition of knowledge is by far one of mostly used technologies in NLP. But, knowledge about word disambiguation has been a bottleneck till now. We integrate rough set based method with IL to acquire knowledge for WSD. Instance-based learning (IL), called example-based, memory-based, or case-based learning, is able to learn outliers in data well Since this is highly desirable for natural language processing in general, IL is widely used in NLP [19][20]. In NLP, when statistical based methods cannot improve accuracy any more, acquiring rules efficiently and automatically, together with processing outliers become an alternative. The RS based knowledge discovery of Chinese multisense verbs we proposed in this paper is, using RS as the mathematic tool, to discover implied context information from part of speech (POS) tagged Chinese text, which determines word meaning and is used in WSD
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