Identifying and Extracting Collocations in Corpus-based Study
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This article aims to identify and extract reasonable collocations. In order to find requirements of reasonable collocations, two approaches, namely one statistical Association Measures and the other near-synonyms relations are focused in this article. BNCweb and Sketch Engine are used, as these software are optimized for identifying and extracting collocations. Associations Measures like Mutual Information, t-score, Log-likelihood, Dice coefficient and logDice are operated and after examining, logDice could be considered the most suitable association measure in terms of both frequency and saliency. When discriminating collocations, grammatical relationships between the node of word and the collocate are helpful to compare collocations effectively and efficiently. In this article, the verbs(demand, claim, request and seek) which are near-synonyms and their collocates are discriminated in relation of subject + verb, object + verb and modifier + verb.
Abstract
This article aims to identify and extract reasonable collocations. In order to find requirements of reasonable collocations, two approaches, namely one statistical Association Measures and the other near-synonyms relations are focused in this article. BNCweb and Sketch Engine are used, as these software are optimized for identifying and extracting collocations. Associations Measures like Mutual Information, t-score, Log-likelihood, Dice coefficient and logDice are operated and after examining, logDice could be considered the most suitable association measure in terms of both frequency and saliency. When discriminating collocations, grammatical relationships between the node of word and the collocate are helpful to compare collocations effectively and efficiently. In this article, the verbs(demand, claim, request and seek) which are near-synonyms and their collocates are discriminated in relation of subject + verb, object + verb and modifier + verb.
참고문헌 (29)
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