Background The large volume of medical literature makes it difficult for healthcare
professionals to keep abreast of the latest studies that support Evidence-Based Medicine …

Abstract Natural Language Processing (NLP) applications on real-life textual content require
a suitable fit for purpose corpora, which can accommodate the ambiguity of the domain …

Parsing is a core natural language processing technique that can be used to obtain the
structure underlying sentences in human languages. Named entity recognition (NER) is the …

Background: Preventing adverse drug reactions (ADRs) is imperative for the safety of the
people. The problem of under-reporting the ADRs has been prevalent across the world …

In this paper, we introduce the T-MexNeg corpus of Tweets written in Mexican Spanish. It
consists of 13,704 Tweets, of which 4895 contain negation structures. We performed an …

We present the full-size Russian complexly NER-labeled corpus of Internet user reviews,
along with an evaluation of accuracy levels reached on this corpus by a set of advanced …

We introduce a multi-label text classifier with per-label attention for the classification of
Electronic Health Records according to the International Classification of Diseases. We …

Based on the free text data of virtual health community, this paper aims to construct a
detection model of adverse drug reaction events by using machine learning. Firstly, this …

The aim of this work is the automatic extraction of Adverse Drug Reactions (ADRs) in
Electronic Health Records (EHRs) written in Spanish. From Natural Language Processing …

Business process management manages the business processes of any organization thus
helps in improving its performance. In any organization, the number of process models in the …