This paper examines the influence of features based on clusters of co-occurrences for supervised Word Sense Disambiguation and Lexical Substitution. Cooccurrence cluster features are derived from clustering the local neighborhood of a target word in a co-occurrence graph based on a corpus in a completely unsupervised fashion. Clusters can be assigned in context and are used as features in a sup...

In machine translation and natural language generation, making the wrong word choice from a set of near-synonyms can be imprecise or awkward, or convey unwanted implications. Using Edmonds’s model of lexical knowledge to represent clusters of near-synonyms, our goal is to automatically derive a lexical knowledge-base from the Choose the Right Word dictionary of near-synonym discrimination. We d...

In machine translation and natural language generation, making the wrong word choice from a set of near-synonyms can be imprecise or awkward, or convey unwanted implications. Using Edmonds’s model of lexical knowledge to represent clusters of near-synonyms, our goal is to automatically derive a lexical knowledge-base from the Choose the Right Word dictionary of near-synonym discrimination. We d...

Linguistic grammars do a good job of elucidating the lexical categories and phrasal units of the languages they model. Certain recurrent Connectionist networks can model similar data but it is not easy to discern the abstract structures of the resulting representations. Hierarchical clustering is helpful in this regard, but it often produces implausible clusters and there seems to be no princip...

This paper presents a framework for indexing and browsing databases of stories, in particular characterizing and visually exploring each narrative’s thematic content. We introduce a method for discovering thematic content in texts via lexical dissimilarity statistics. A maximumlikelihood algorithm clusters words into pools of similar meaning, using a thesaurus for rough estimates of word sense ...

Abstract This study explored the developmental trajectories of L2 Lexical-semantic networks by comparing structure lexical-semantic Chinese EFL learners at two levels English: intermediate and advanced. Analyses data from semantic fluency tasks found that with improvement language proficiency, network becomes more connected but less centralized. community revealed advanced has denser clusters t...

Document clustering is an unsupervised machine learning method that separates a large subject heterogeneous collection (Corpus) into smaller, more manageable, subject homogeneous collections (clusters). Traditional method of document clustering works around extracting textual features like: terms, sequences, and phrases from documents. These features are independent of each other and do not cat...