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researcher

A. Jorge

12 papers hereh-index 285.1k citations254 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4
  • last author7

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.AI2
  • cs.IR2
  • cs.LG1
  • cs.OH1
  • physics.geo-ph1
same name
  • A. Jorge — 4 papers, h 6
  • A. Jorge — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152024
most citedEvaluation of recommender systems in streaming environments

30 citations · 50 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2021

Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning

Sofia Oliveira, Daniel Loureiro, Alípio Jorge

The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed fo…

cs.CL2019★ 2 cited

Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation

Daniel Loureiro, Alipio Jorge

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering t…

cs.CL2019★ 2 cited

LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)

Daniel Loureiro, Alipio Jorge

This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Se…

cs.CL2018

Affordance Extraction and Inference based on Semantic Role Labeling

Daniel Loureiro, Alípio Mário Jorge

Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain w…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.