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Found 12 papers, 3 papers with code
The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project
We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages.
HitzalMed: Anonymisation of Clinical Text in Spanish
HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task.
NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts
This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish).
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT
Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy.
Hate Speech Dataset from a White Supremacy Forum
Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic.
Biomedical term normalization of EHRs with UMLS
This paper presents a novel prototype for biomedical term normalization of electronic health record excerpts with the Unified Medical Language System (UMLS) Metathesaurus.
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever.
Multilingual CALL Framework for Automatic Language Exercise Generation from Free Text
This paper describes a web-based application to design and answer exercises for language learning.
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings
In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity.
Highlighting relevant concepts from Topic Signatures
Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate WordNet sense to large sets of topically related words acquired from the web, named TSWEB.
