This paper proposes new algorithms to compute the sense similarity between two units (words, phrases, rules, etc.) from parallel corpora. The sense similarity scores are computed by using the vector space model. We then apply the algorithms to statistical machine translation by computing the sense similarity between the source and target side of translation rule pairs. Similarity scores are use...

Our thesis proposal aims at integrating word similarity measures in pattern ranking for relation extraction bootstrapping algorithms. We note that although many contributions have been done on pattern ranking schemas, few explored the use of word-level semantic similarity. Our hypothesis is that word similarity would allow better pattern comparison and better pattern ranking, resulting in less ...

We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and discuss the k-Means and EM algorithms, based on text similarity, bibliographic coupling, and co-citation strength. We study the utilization of the principle of multi-view learning to combine these similarity measures. We explor...

The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we pres...

Clustering is an unsupervised technique dealing with problems of organizing a collection of patterns into clusters based on similarity. Most clustering algorithms are based on hierarchical and partitional approaches. Algorithms based on an hierarchical approach generate a dendrogram representing the nested grouping of patterns and similarity levels at which groupings change [19]. Partitional cl...

The indexing algorithms and data structures for similarity searching in metric spaces seem to emerge from a great diversity, and diierent approaches have been proposed and analyzed separately, often under diierent assumptions. Currently, the only realistic way to compare two diierent algorithms is to apply them to the same data set. We present a uniied model for studying similarity searching al...

The indexing algorithms and data structures for similarity searching in metric spaces seem to emerge from a great diversity, and diierent approaches have been proposed and analyzed separately, often under diierent assumptions. Currently, the only realistic way to compare two diierent algorithms is to apply them to the same data set. We present a uniied model for studying similarity searching al...

In this paper we present a comparison of multiple cluster algorithms and their suitability for clustering text data. The clustering is based on similarities only, employing the Kolmogorov complexity as a similiarity measure. This motivates the set of considered clustering algorithms which take into account the similarity between objects exclusively. Compared cluster algorithms are Median kMeans...