Computational Model Of Chinese Unconventional Semantic Collocations Oriented To Metaphor Computation

Metaphor, an ordinary everyday phenomenon of language use, has become the focus of mind and language mechanism. The comprehension of metaphor by machine will be a bottleneck problem in natural language understanding and machine translation. Therefore, great attention has been paid to computational mechanisms of metaphor within the last few decades.This paper researches on classified recognition of unconventional semantic collocations, which is one of most important types of metaphor recognition. Proposed an example-based classified recognition model of unconventional semantic collocations, and achieved a Chinese Metaphor Recognition System.Our research includes the following aspects:1. Introduced the background and significance of our research. And made a summarization of existing research results in this field, including semantic collocation and metaphor recognition.2. Made the semantic analysis and introduced the definition, classification and characteristics of unconventional collocations.3. Proposed the example-based classified recognition model of unconventional semantic collocations. Introduced the basic idea of this model, and the method of constructing the semantic example model bank. As were as given the experimental results of precision and recall.4. The application of semantic unconventional collocations used in Chinese metaphor computation. Introduced the Metaphor Role Dependency Schema, the construction of Chinese Metaphor Corpus, the embedded tree matching algorithm based on metaphorical dependency structure, and the design of Chinese Metaphor Recognition System.Our research of semantic unconventional collocations is a good innovation, and according to the experimental results, shows that the method is feasible.