Clustering words is a widely used technique in statistical natural language processing. It requires syntactic, semantic, and contextual features. Especially, semantic clustering is gaining a lot of interest. It consists in grouping a set of words expressing the same idea or sharing the same semantic properties. In this paper, we present a new method to integrate semantic classes in a Statistica...

Abstract Introducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored proven still be useful case sequence-to-sequence systems. In this work, we investigate whether gains hold state-of-the-art architecture neural translation, Transformer, instead recurrent architectures. We propose a new model, Fact...

Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicateargument structures for statistical machine translation: 1) a predicate translation model and 2) an argument reordering model. The predicate translation model explores lexical ...

abstractthe aim of this study was to find the appropriate type of translation of english brand names iranian consumers prefer, i.e. phonetic, semantic and phonosemantic types of translation. also, it was aimed at examining the type of font size iranian consumers tends to see on packages of english products. to this end, the distinction between male and female participants as well as the partici...

Toward Semantic Machine Translation

We present a comparative evaluation of two data-driven models used in translation selection of English-Korean machine translation. Latent semantic analysis(LSA) and probabilistic latent semantic analysis (PLSA) are applied for the purpose of implementation of data-driven models in particular. These models are able to represent complex semantic structures of given contexts, like text passages. G...