Marianna Apidianaki

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: A unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory is presented.

TL;DR: An unsupervised WSD method and a lexical selection method that exploit the results of a data-driven sense induction method and it is shown how this automatically acquired information can be exploited for a multilingual WSD and MT evaluation more sensitive to lexical semantics.

TL;DR: The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references, and achieves very high correlations with human ratings.

TL;DR: This article proposes to operationalize partitionability as clusterability, a measure of how easy the occurrences of a lemma are to cluster, and test two ways of measuring clusterability: existing measures from the machine learning literature that aim to measure the goodness of optimal k-means clusterings, and the idea that if aLemma is more clusterable, two clusterings based on two different “views” of the same data points will be more congruent.