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Regular version of the site

Book chapter

Automatic Detection of Stable Grammatical Features in N-Grams

P. 73-81.
Kopotev M., Pivovarova L., Kochetkova N. A., Yangarber R.

This paper presents an algorithm that allows the user to issue a query pattern, collects multi-word expressions (MWEs) that match the pattern, and then ranks them in a uniform fashion. This is achieved by quantifying the strength of all possible relations between the tokens and their features in the MWEs. The algorithm collects the frequency of morphological categories of the given pattern on a unified scale in order to choose the stable categories and their values. For every part of speech, and for all of its categories, we calculate a normalized Kullback-Leibler divergence between the category’s distribution in the pattern and its distribution in the corpus overall. Categories with the largest divergence are considered to be the most significant. The particular values of the categories are sorted according to a frequency ratio. As a result, we obtain morphosyntactic profiles of a given pattern, which includes the most stable category of the pattern, and their values.

In book

Atlanta: The Association for Computational Linguistic, 2013.