Probabilistic topic models are unsupervised generative models which model document
content as a two-step generation process, that is, documents are observed as mixtures of …

In the last few years, there has been growing interest in aspect-based sentiment analysis,
which deals with extracting, clustering, and rating the overall opinion about the features of …

As cross-lingual information retrieval is attracting increasing attention, tools that measure
cross-lingual semantic similarity between documents are becoming desirable. In this paper …

While building and using a fully semantic understanding of Web contents is a distant goal,
named entities (NEs) provide a small, tractable set of elements carrying a well-defined …

In this paper, we present a feature-based method to align documents with similar content
across two sets of bilingual comparable corpora from daily news texts. We evaluate the …

Abstract Statistical Machine Translation (SMT) relies on the availability of rich parallel
corpora. However, in the case of under-resourced languages, parallel corpora are not …

This paper is focused on discovering bilingual news clusters in a comparable corpus.
Particularly, we deal with the news representation and with the calculation of the similarity …

Call Centers have always played a highly significant role in the service industry, from retail
to technical support. Government of India (GOI) has launched Kisan Call Centers (KCC) …

The idea of automatic extraction of important information from text documents comes from
the time of first steps in the natural language processing. Its importance rapidly grows with …

This paper presents a language-independent Multilingual Document Clustering (MDC)
approach on comparable corpora. Named entites (NEs) such as persons, locations …