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Abstract
In all natural languages, some words collocate with other words to create multi-worded blocks of meaning – the collocations. Since identification of collocations is vital for information retrieval, language learning, psycholinguistics, authorship determination and translation, collocation extraction is an important issue in natural language processing. In this paper we present a method which is designed to improve current statistical methods that generate ranked lists of collocation candidates.
Due to meaning integrity, any word in a collocation must suggest or at least imply the subsequent words composing the collocation. As a result, we may state that the words in a random text differ in the tendency to facilitate the prediction of the next word. If a word helps the prediction then it tends to collocate, otherwise it does not. In this paper, an attempt has been made to extract collocations by measuring collocation tendency of words and word combinations. The method used is to filter out free word pairs (the words that do not facilitate the prediction of the next word or those in which meaning integrity has not been completed yet) in the lists of candidate pairs.
Collocation tendency method is tested on a base data set extracted by some statistical collocation extraction techniques (frequency of occurrence, point-wise mutual information, the t-test, chi-square techniques) and is evaluated by precision and recall measures. We have found that collocation tendency method brings a remarkable improvement on frequency of occurrence and the t-test techniques.
Notes
1A group of two consecutive words in text or speech.