TCtract-A Collocation Extraction Approach for Noun Phrases Using Shallow Parsing Rules and Statistic Models

This paper presents a hybrid method for extracting Chinese noun phrase collocations that combines a statistical model with rule-based linguistic knowledge. The algorithm first extracts all the noun phrase collocations from a shallow parsed corpus by using syntactic knowledge in the form of phrase rules. It then removes pseudo collocations by using a set of statistic-based association measures (AMs) as filters. There are two main purposes for the design of this hybrid algorithm: (1) to maintain a reasonable recall while improving the precision, and (2) to investigate the proposed association measures on Chinese noun phrase collocations. The performance is compared with a pure statistical model and a pure rule-based method on a 60MB PoS tagged corpus. The experiment results show that the proposed hybrid method has a higher precision of 92.65% and recall of 47% based on 29 randomly selected noun headwords compared with the precision of 78.87% and recall of 27.19% of a statistics based extraction system. The F-score improvement is 55.7%.