This article brings two major contributions. First, we present the results of a critical analysis
of recent scientific articles about neural and non-neural approaches and representations for …

News has become a very important need in modern society. Almost every level of society
needs information such as news. Online news gets the attention of writers because there are …

This is the first work to investigate the effectiveness of BERT-based contextual embeddings
in active learning (AL) tasks on cold-start scenarios, where traditional fine-tuning is …

We investigate two essential challenges in the context of Hierarchical Topic Modeling (HTM)–
(i) the impact of data representation and (ii) topic evaluation. The data representation directly …

Topic Modeling (TM) is among the most exploited approaches to extracting and organizing
information from large amounts of data. Basically, these approaches aim to find semantic …

In this paper a novel approach for effective topic modeling is presented. The approach is
different from traditional vector space model-based topic modeling, where the Bag of Words …

In this paper, we describe our participation in two tasks organized by WOSP 2020,
consisting of classifying the context of a citation (eg, background, motivational, extension) …

Pharmaceutical patents play an important role by protecting the innovation from copies but
also drive researchers to innovate, create new products, and promote disruptive innovations …

Topic detection in social media is a challenging task due to large-scale short, noisy and
informal nature of messages. Most existing methods only consider textual content or …

The tagging aims to address a challenge to search relevant text-documents given a set of
tags. In addition, the tag-based approaches received a wide attention as a possible solution …