Most words in natural languages are polysemous in nature that is they have multiple possible meanings or senses. The sense in which the word is used determines the translation of the word. We show that incorporating a sense-based translation model into statistical machine translation model consistently improves translation quality across all different test sets of five different language-pairs,...

Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, ...

Previous research with the visual lexical decision task demonstrated that polysemous words (multiple related senses) have a processing advantage when compared to unambiguous words, whereas homonymous unrelated meanings) disadvantage. Although same pattern of results was observed in Serbian, two effects were investigated separate studies. The aim this study test whether can be replicated both ty...

Polysemy is one of the complex semantic phenomena that have been tackled by many scholars from different points view. Different models suggested about how polysemous words are represented in mental lexicon. One these sophisticated proposed Tyler and Evans (2003). It seems this model has some problems paper directed to solve. The based on two questions: (1) what main aspects network meanings reg...

This paper proposes a two-phase word sense disambiguation method, which filters only the relevant senses by utilizing the multiword expression and then disambiguates the senses based on Weight Distribution Model. Multiword expression usually constrains the possible senses of a polysemous word in a context. Weight Distribution Model is based on the hypotheses that every word surrounding a polyse...

Unlike the problems of part-of-speech tagging and parsing, where commonly utilized training and test sets such as the Brown Corpus and Penn Treebank have existed for a number of years, evaluation of word sense disambiguation sytems is not yet standardized. In fact, most previous work in sense disambiguation has tended to use different sets of polysemous words, different sense inventories, diffe...

An A-C bilingual dictionary can be inferred by merging A-B and B-C dictionaries using B as pivot. However, polysemous pivot words often produce wrong translation candidates. This paper analyzes two methods for pruning wrong candidates: one based on exploiting the structure of the source dictionaries, and the other based on distributional similarity computed from comparable corpora. As both meth...

We describe a technique for adding contextual distinctions to word embeddings by extending the usual embedding process — into two phases. The first phase resembles existing methods, but also constructs K classifications of concepts. The second phase uses these classifications in developing refined K embeddings for words, namely word K-embeddings. The technique is iterative, scalable, and can be...

Inter-word associations like stagger drunken, or intra-word sense divisions (e.g. write a diary vs. write an article) are difficult to compile using a traditional lexicographic approach. As an alternative, we present a model that reflects this kind of subtle lexical knowledge. Based on the minimal sense of a word (clique), the model (1) selects contextually related words (contexonyms) and (2) c...