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In this paper, we describe our experiments on statistical word sense disambiguation (WSD) using two systems based on different ap- proaches: Na¨ıve Bayes on word tokens and Max- imum Entropy …...

We explore the performance of the Vector Space Model (VSM) in finding verb synonyms in Por- tuguese by analyzing the impact of three oper- ating parameters: (i) the weighting function, …...

This paper presents a large-scale evalua- tion of bag-of-words distributional models on two datasets from priming experiments involving syntagmatic and paradigmatic relations. We interpret the variation in performance achieved by …...

AbstractIn this paper, we propose a method that combines the principles of automatic term recognition and the distributional hypothesis to identify technology terms from a corpus of scientific publications. We …...

AbstractA salient feature of Neural Machine Translation (NMT) is the end-to-end nature of training employed, eschewing the need of separate components to model different linguistic phenomena. Rather, an NMT model …...

AbstractMany researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding. The relationship between intrinsic and extrinsic evaluation, however, has only been …...

AbstractExisting approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on …...