The problem of the multiplicity of word meanings has preoccupied so many researches from the linguistics, psychology or computational linguistic. In this paper, we revised how LSA represents the polysemous words and we explain some bias related with the meaning generation and revised some constraint-satisfaction models which introduce into the equation some dynamic mechanisms. The idea of these...

Recent research in psycholinguistics suggests that language processing frequently involves mental imagery. This paper focuses on visual imagery and discusses two issues regarding the of polysemous words (i.e.words with multiple related meanings or senses) — co-predication sense-relatedness. It aims to show how can illuminate these issues.

We describe a method for automatic word sense disambiguation using a text corpus and a machine-readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in...

We describe a method for automatic word sense disambiguation using a text corpus and a machinereadable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in ...

Polysemous words acquire different senses and meanings from their contexts. Representing words in vector space as a function of their contexts captures some semantic and syntactic features for words and introduces new useful relations between them. In this paper, we exploit different vectorized representations for words to solve the problem of Cross Lingual Lexical Substitution. We compare our ...

Polysemous words have different but related meanings (senses), such as paper meaning a newspaper or writing material. Six experiments examined the similarity of word senses using categorization and inference tasks. The experiments found that subjects did not categorize together phrases that used a polysemous word in different senses, though they did when the word was used in the same sense. Dif...

A quantitative analysis of the English lexicon was done in the paper. The three electronic dictionaries are under examination: the English Wiktionary, WordNet, and the Russian Wiktionary. It was calculated the quantity of English words and meanings (senses) in these dictionaries. The distribution of words for each part of speech, the quantity of monosemous and polysemous words and the distribut...

Word sense induction is an unsupervised task to find and characterize different senses of polysemous words. This work investigates two unsupervised approaches that focus on using distributional word statistics to cluster the contextual information of the target words using two different algorithms involving latent dirichlet allocation and spectral clustering. Using a large corpus for achieving ...

Semantic ambiguity has often been divided into 2 forms: homonymy, referring to words with 2 unrelated interpretations (e.g., bark), and polysemy, referring to words associated with a number of varying but semantically linked uses (e.g., twist). Typically, polysemous words are thought of as having a fixed number of discrete definitions, or "senses," with each use of the word corresponding to one...