Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we a...

Word finding difficulty is a known impairments in multiple sclerosis (MS). The purpose of this study is to adapt homophone meaning generation test to Persian language, and then examine word storage and access in multiple sclerosis patients through these three word-finding tests. This study examined the word retrieval in 90 Persian speaking patients with multiple sclerosis and 90 matched healthy...

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning...

The simultaneous clustering of documents and words, known as co-clustering, has proved to be more effective than one-sided in dealing with sparse high-dimensional datasets. By their nature, text data are also generally unbalanced directional. Recently, the von Mises–Fisher (vMF) mixture model was proposed handle while harnessing directional nature text. In this paper, we propose a general co-cl...

We will demonstrate the output of a distributional clustering algorithm called Clustering by Committee that automatically discovers word senses from text1.

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

In this chapter, we discuss the use of Self Organizing Maps (SOM) to deal with various tasks in Document Image Analysis. The SOM is a particular type of artificial neural network that computes, during the learning, an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning in document image ana...

Unsupervised word clustering algorithms — which form word clusters based on a measure of distributional similarity — have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word clusters as factors in statistical machine translation. Although some of the language pairs in this w...