DOI

The paper deals with collocation extraction from corpus data. The experiments are described with the objective to study collocation extraction based on statistical association measures. A whole number of formulas have been created to integrate different factors that determine the association between the collocation components. The experiments are described whose objective was to study the method of collocation extraction based on the statistical association measures. The paper is focused on bigram collocations. The obtained data on the measure precision allows to establish to some degree that in cases when collocation extraction is not used for some special purposes such measures as MI.l-og_f, log-Dice, minimum sensitivity should be used. At the same time, various options of their integration are desirable and useful. To use advantages of separate measures, we offer to create a combined list of collocations extracted by different measures and propose a number of parameters that allow to rank collocates in a combined list in some reasonable way.
Язык оригиналаанглийский
Название основной публикацииProceedings of the International Conference IMS-2017 (St. Petersburg; Russian Federation, 21-24 June 2017)
Страницы125-134
Число страниц10
DOI
СостояниеОпубликовано - 2017
СобытиеXX Международная объединенная научная конференция «Интернет и современное общество»: международная объединенная конференция - Университет ИТМО, Санкт-Петербург, Российская Федерация
Продолжительность: 21 июн 201723 июн 2017
Номер конференции: XX
http://icims.ifmo.ru/
http://ims.ifmo.ru/ru/pages/28/IMS_2017.htm

Серия публикаций

НазваниеACM INTERNATIONAL CONFERENCE PROCEEDINGS SERIES

конференция

конференцияXX Международная объединенная научная конференция «Интернет и современное общество»
Сокращенное названиеIMS-2017
Страна/TерриторияРоссийская Федерация
ГородСанкт-Петербург
Период21/06/1723/06/17
Сайт в сети Internet

    Области исследований

  • metrics, Collocation extraction; , association measures; , evaluation; , correlation; , ranking,

ID: 34962509