Gülşen Eryiğit

TL;DR: This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015, which attracted 245 submissions from 29 teams and provided 19 training and 20 testing datasets.

TL;DR: Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

TL;DR: A shared understanding of what is meant by “MWE processing” is offered, distinguishing the subtasks of MWE discovery and identification, and the interactions between MWE processing and two use cases: Parsing and machine translation are elucidated.

TL;DR: An investigation of data-driven dependency parsing of Turkish, an agglutinative, free constituent order language that can be seen as the representative of a wider class of languages of similar type, shows that morphological structure plays an essential role in finding syntactic relations in such a language.