NER and Open Information Extraction for Portuguese: Notebook for IberLEF 2019 Portuguese Named Entity Recognition and Relation Extraction Tasks

In order to illustrate this behaviour, Table 4 shows the results obtained by our system on the development set provided by organizers in subtask B (rela- tion extraction), as we

Scenario 2) Test 2 dataset contains the target relations to be identified, but we matched the extractions performed by the participant systems against the relation in test 2 and

In this work, we propose a system based on different deep learning architectures, similar to that was used by [30]: a Bidirectional Long Short-Term Memory (BiL- STM)[10] NER model

The organization of the IberLEF (Iberian Languages Evaluation Forum) forum proposes a task that involves the automatic extraction of any relation descriptor expressing any

The extractions of the systems were matched against the relationship in Test 1 golden dataset, and metrics of exact Precision (EP), exact Recall (ER), partial Precision (PP),

Having this in mind, it is in our plans to develop new versions of FactPyPort, following alternative approaches, covering Open Information Extraction [7], or training a CRF for

We have used TensorFlow code based on tutorial code released by Neural Machine Translation 1 [11] that was developed based on Sequence-to-Sequence (Seq2Seq) models [21, 1, 12]

This paper presents a strategy to integrate the results provided by different EEL approaches to extract and link named entities from unstructured English text.. The proposed strategy