@inproceedings{b528a577df7949c1ad9a7d24bcbe35d0,
title = "Automatic Identification of Multi-Word Expressions for Latvian and Lithuanian",
abstract = "We discuss an experiment on automatic identification of bi-gram multiword expressions for Latvian and Lithuanian. As these languages are considered to be underresourced in terms of lexical resources and availability or accuracy of special lexical tools (e.g. POS-tagger, parser), our approach uses raw corpora and combination of lexical association measures and supervised machine learning. We have achieved 92,4% precision and 52,2% recall for Latvian and 95,1% precision and 77,8% recall - for Lithuanian..",
keywords = "Hybrid approach, Lexical association measures, Machine learning, Multi-word expressions",
author = "Justina Mandravickait and Tomas Krilaviius and Man, {Ka Lok}",
year = "2017",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "706--709",
editor = "Oscar Castillo and Ao, {S. I.} and Craig Douglas and Feng, {David Dagan} and Korsunsky, {A. M.}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017",
note = "2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 ; Conference date: 15-03-2017 Through 17-03-2017",
}