In [12] a novel approach to Web search result clustering based on Word Sense Induction, i.e. the automatic discovery of word senses from raw text was presented; key to the proposed approach is the idea of, first, automatically inducing senses for the target query and, second, clustering the search results based on their semantic similarity to the word senses induced. In [1] we proposed an innov...

We propose an unsupervised approach to POS tagging where first we associate each word type with a probability distribution over word classes using Latent Dirichlet Allocation. Then we create a hierarchical clustering of the word types: we use an agglomerative clustering algorithm where the distance between clusters is defined as the JensenShannon divergence between the probability distributions...

We propose an unsupervised approach to POS tagging where first we associate each word type with a probability distribution over word classes using Latent Dirichlet Allocation. Then we create a hierarchical clustering of the word types: we use an agglomerative clustering algorithm where the distance between clusters is defined as the JensenShannon divergence between the probability distributions...

Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new sign...

Our research addresses the task of updating legal documents when new information emerges. In this paper, we employ a hierarchical ranking model to the task of updating legal documents. Word clustering features are incorporated to the ranking models to exploit semantic relations between words. Experimental results on legal data built from the United States Code show that the hierarchical ranking...

Studies of isolated word recognition systems have shown that a set of carefully chosen templates can be used to bring the performance of speaker-independent systems up to that of systems trained to the individual speaker. The earliest work in this area used a sophisticated set of pattern recognition algorithms in a human-interactive mode to create the set of templates (multiple patterns) for ea...

In this paper, we present a constrained co-clustering approach for clustering textual documents. Our approach combines the benefits of information-theoretic co-clustering and constrained clustering. We use a two-sided hidden Markov random field (HMRF) to model both the document and word constraints. We also develop an alternating expectation maximization (EM) algorithm to optimize the constrain...

background: finding the right word is a necessity in communication, and its evaluation has always been a challenging clinical issue, suggesting the need for valid and reliable measurements. the homophone meaning generation test (hmgt) can measure the ability to switch between verbal concepts, which is required in word retrieval. the purpose of this study was to adapt and validate the persian ve...

In this paper, we propose an approach for constructing clusters of related terms that may be used for deriving formal conceptual structures in a later stage. In contrast to previous approaches in this direction, we explicitly take into account the fact that words can have different, possibly even unrelated, meanings. To account for such ambiguities in word meaning, we consider two alternative s...