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In this paper we approach word sense disambiguation and information extrac- tion as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 …...

In this paper, we introduce a hybrid method to associate English collocations with sense class members chosen from WordNet. Our combinational approach includes a learning-based method, a paraphrase-based method and …...

In this paper, we propose a new method for semantic class induction. First, we in- troduce a generative model of sentences, based on dependency trees and which takes into account …...

We present two Twitter datasets annotated with coarse-grained word senses (super- senses), as well as a series of experiments with three learning scenarios for super- sense tagging: weakly supervised learn- …...

This paper presents our system submitted for SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiM- SUM; Schneider, Hovy, et al., 2016). We extend AMALGrAM (Schneider and …...

AbstractSelecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good …...

This paper describes our approach towards the SemEval-2016 Task 10: Detecting Mini- mal Semantic Units and their Meanings (DiM- SUM). We consider that the two problems are similar to multiword …...

AbstractContextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. …...