Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We propose a new method that uses a dictionary to learn models of visual word sense from a large collection of unlabeled web data. The use of LDA to discover a latent sense space makes the model robust despite the very l...

Verbal Polysemy Resolution through Contextualized Clustering of Arguments A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Anna A. Rumshisky Natural language is characterized by a high degree of polysemy, and the majority of content words accept multiple interpretations. However, this does not significantly com...

We describe results of a word sense annotation task using WordNet, involving half a dozen well-trained annotators on ten polysemous words for three parts of speech. One hundred sentences for each word were annotated. Annotators had the same level of training and experience, but interannotator agreement (IA) varied across words. There was some effect of part of speech, with higher agreement on n...

reaching a considerable growth in terms of rhetoric, eloquence, and semantic richness of words or terms, the arabic language was ready in the pre-islamic period to receive the divine revelation. on the other hand, the qur’an (as the word of allah) with its sublime meanings and boundless scientific facts had to be revealed in the arabic language in such a way that it would succeed in duly commun...

In this paper we propose a new approach to the generation of pseudowords, i.e., artificial words which model real polysemous words. Our approach simultaneously addresses the two important issues that hamper the generation of large pseudosense-annotated datasets: semantic awareness and coverage. We evaluate these pseudowords from three different perspectives showing that they can be used as reli...

Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We propose a new method that uses a dictionary to learn models of visual word sense from a large collection of unlabeled web data. The use of LDA to discover a latent sense space makes the model robust despite the very l...

Words like church are polysemous, having two related senses (a building and an organization). Three experiments investigated how polysemous senses are represented and processed during sentence comprehension. On one view, readers retrieve an underspecified, core meaning, which is later specified more fully with contextual information. On another view, readers retrieve one or more specific senses...

Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algori thm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have...

Arguing that various ways of using context in word sense disambiguation (WSD) can be considered as distinct representations of a polysemous word, a theoretical framework for the weighted combination of soft decisions generated by experts employing these distinct representations is proposed in this paper. Essentially, this approach is based on the Dempster-Shafer theory of evidence. By taking th...

Polysemy is pervasive in natural languages, and affects both content and function words. While deciding which sense is intended on a given occasion of use rarely seems to cause any difficulty for speakers of a language, polysemy has proved notoriously difficult to treat both theoretically and empirically. Some of the questions that have occupied linguists, philosophers and psychologists interes...