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Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation
The current approaches for machine translation usually require large set of parallel corpus in order to achieve fluency like in the case of neural machine translation (NMT), statistical machine translation (SMT) and example-based machine translation (EBMT).
CogALex-V Shared Task: HsH-Supervised -- Supervised similarity learning using entry wise product of context vectors
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation.
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
Learning Thesaurus Relations from Distributional Features
The similarity of words can be computed by comparing their feature vectors.
