Search Results for author:
Found 11 papers, 4 papers with code
Priberam Labs at the 3rd Shared Task on SlavNER
This document describes our participation at the 3rd Shared Task on SlavNER, part of the 8th Balto-Slavic Natural Language Processing Workshop, where we focused exclusively in the Named Entity Recognition (NER) task.
Supervising the Centroid Baseline for Extractive Multi-Document Summarization
The centroid method is a simple approach for extractive multi-document summarization and many improvements to its pipeline have been proposed.
Improving abstractive summarization with energy-based re-ranking
Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations).
Simplifying Multilingual News Clustering Through Projection From a Shared Space
Our system achieves state-of-the-art results on a multilingual news stream clustering dataset, and we introduce a new evaluation for zero-shot news clustering in multiple languages.
An NLP Solution to Foster the Use of Information in Electronic Health Records for Efficiency in Decision-Making in Hospital Care
The project aimed to define the rules and develop a technological solution to automatically identify a set of attributes within free-text clinical records written in Portuguese.
Mapping molecular complexes with Super-Resolution Microscopy and Single-Particle Analysis
Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease.
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments.
Priberam Labs at the NTCIR-15 SHINRA2020-ML: Classification Task
Wikipedia is an online encyclopedia available in 285 languages.
Hierarchical Nested Named Entity Recognition
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions.
Automated Fact Checking in the News Room
Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation.
Jointly Extracting and Compressing Documents with Summary State Representations
We present a new neural model for text summarization that first extracts sentences from a document and then compresses them.
