The number of applications being developed that require access to knowledge about the
real world has increased rapidly over the past two decades. Domain ontologies, which …

Predicting future pairs of the acquirer and acquiree companies is important for acquisition or
investment strategy. This prediction is a challenging problem due to the following …

The increasing volume of textual information on any topic requires its compression to allow
humans to digest it. This implies detecting the most important information and condensing it …

The automatic text summarization (ATS) task consists in automatically synthesizing a
document to provide a condensed version of it. Creating a summary requires not only …

This study proposes a language and domain independent approach for automatic extractive
text summarization (EATS) tasks, which is based on a clustering scheme supported by a …

This paper introduces a measure of the proximity in ideas using unsupervised machine
learning. Knowledge transfers are considered a key driving force of innovation and regional …

Common approaches to problems involving multiple modalities (classification, retrieval,
hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from …

In this paper we perform a comparative analysis of three models for a feature representation
of text documents in the context of document classification. In particular, we consider the …

Purpose This paper aims to investigate how digitalization empowers government auditing
with technical power to serve national governance better. Design/methodology/approach …

Using a simple vector in R n is a traditional way of representing documents in vector spaces.
However, this representation tends to ignore the discourse and syntactic structure of texts. A …