We consider a challenging clustering task: the clustering of multi-word terms without document co-occurrence information in order to form coherent groups of topics. For this task, we developed a methodology taking as input multi-word terms and lexico-syntactic relations between them. Our clustering algorithm, named CPCL is implemented in the TermWatch system. We compared CPCL to other existing ...

Web search result clustering aims to facilitate information search on the Web. Rather than presenting the results of a query as a flat list, these are grouped on the basis of their similarity and subsequently shown to the user as a list of possibly labeled clusters. Each cluster is supposed to represent a different meaning of the input query, thus taking into account the language ambiguity issu...

Recent studies in word sense induction are based on clustering global co-occurrence vectors, i.e. vectors that reflect the overall behavior of a word in a corpus. If a word is semantically ambiguous, this means that these vectors are mixtures of all its senses. Inducing a word’s senses therefore involves the difficult problem of recovering the sense vectors from the mixtures. In this paper we a...

This article proposes offline language-free writer identification based on speeded-up robust features (SURF), goes through training, enrollment, and identification stages. In all stages, an isotropic Box filter is first used to segment the handwritten text image into word regions (WRs). Then, the SURF descriptors (SUDs) of word region and the corresponding scales and orientations (SOs) are extr...

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 objective of this project is to analyze the performance of a class-based language model and compare it to the performance of traditional n-gram language models. Class-based language models are well-studied, as is the use of clustering to learn classes of words. However, it seems fairly standard across the literature to use hard-clustering i.e. assign each word to a single class and then to ...

Document clustering is unsupervised machine learning technique that, when provided with a large document corpus, automatically sub-divides it into meaningful smaller sub-collections called clusters. Currently, document clustering algorithms use sequence of words (terms) to compactly represent documents and define a similarity function based on the sequences. We believe that the word sequence is...

Clustering methods have been extensively used in the solution of many Information Processing tasks in order to capture unknown object categories. This paper presents an approach to Word Sense Disambiguation based on clustering. The underlying idea is that the clustering of word senses provides a useful way to discover semantically related senses. We evaluate our proposal regarding both fineand ...