Abstract
A challenging task in Chinese collocation extraction is to improve both the precision and recall rate. Most lexical statistical methods including Xtract face the problem of unable to extract collocations with lower frequencies than a given threshold. This paper presents a method where HowNet is used to find synonyms using a similarity function. Based on such synonym information, we have successfully extracted synonymous collocations which normally cannot be extracted using the lexical statistical approach. We applied synonyms mapping to each headword to extract more synonymous word bi-grams. Our evaluation over 60MB tagged corpus shows that we can extract synonymous collocations that occur with very low frequency, sometimes even for collocations that occur only once in the training set. Comparing to a collocation extraction system based on Xtract, we have reached the precision rate of 43% on word bi-grams for a set of 9 headwords, almost 50% improvement from precision rate of 30% in the Xtract system. Furthermore, it improves the recall rate of word bi-gram collocation extraction by 30%.
| Original language | English |
|---|---|
| Pages | 86-93 |
| Number of pages | 8 |
| State | Published - 2004 |
| Externally published | Yes |
| Event | 3rd SIGHAN Workshop on Chinese Language Processing, SIGHAN@ACL 2004 - Barcelona, Spain Duration: 25 Jul 2004 → … |
Conference
| Conference | 3rd SIGHAN Workshop on Chinese Language Processing, SIGHAN@ACL 2004 |
|---|---|
| Country/Territory | Spain |
| City | Barcelona |
| Period | 25/07/04 → … |
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