18 citations · 49 across the 10 of their papers we have counts for
7 papers · 1 filter
Named Entity Recognition on Noisy Data using Images and Text (1-page abstract)
Diego Esteves
Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limit…
DeFactoNLP: Fact Verification using Entity Recognition, TFIDF Vector Comparison and Decomposable Attention
Aniketh Janardhan Reddy, Gil Rocha, Diego Esteves
In this paper, we describe DeFactoNLP, the system we designed for the FEVER 2018 Shared Task. The aim of this task was to conceive a system that can not only automatically assess t…
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
Diego Esteves, Aniketh Janardhan Reddy, Piyush Chawla +1
With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-maki…
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz +5
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing an…
Neural Machine Translation for Query Construction and Composition
Tommaso Soru, Edgard Marx, André Valdestilhas +3
Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural mach…
Expeditious Generation of Knowledge Graph Embeddings
Tommaso Soru, Stefano Ruberto, Diego Moussallem +4
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddi…
LIDIOMS: A Multilingual Linked Idioms Data Set
Diego Moussallem, Mohamed Ahmed Sherif, Diego Esteves +2
In this paper, we describe the LIDIOMS data set, a multilingual RDF representation of idioms currently containing five languages: English, German, Italian, Portuguese, and Russian.…