Automatic Content Extraction for Portuguese

Item Name: Automatic Content Extraction for Portuguese
Author(s): Luís Filipe Cunha, Ricardo Campos, Alípio Jorge
LDC Catalog No.: LDC2024T05
ISLRN: 802-512-969-698-4
DOI: https://doi.org/10.35111/8gr4-tn81
Release Date: May 15, 2024
Member Year(s): 2024
DCMI Type(s): Text
Data Source(s): broadcast conversation, broadcast news, newsgroups, telephone conversations, weblogs
Project(s): ACE
Application(s): automatic content extraction
Language(s): Portuguese
Language ID(s): por
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2024T05 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Cunha, Luís Filipe, Ricardo Campos, and Alípio Jorge. Automatic Content Extraction for Portuguese LDC2024T05. Web Download. Philadelphia: Linguistic Data Consortium, 2024.
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Introduction

Automatic Content Extraction for Portuguese (LDC2024T05) was developed at INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência and consists of automatic Brazilian Portuguese and European Portuguese translations of the English text and annotations in ACE 2005 Multilingual Training Corpus (LDC2006T06).

ACE 2005 Multilingual Training Corpus was developed by LDC to support the Automatic Contract Extraction (ACE) program, specifically, by providing training data for the 2005 technology evaluation. It contains 1,800 files of mixed genre text in English, Arabic and Chinese annotated for entities, relations and events. The objective of the ACE program was to develop automatic content extraction technology to support automatic processing of human language in text form. Text genres included newswire, broadcast news, broadcast conversation, weblog, discussion forums, and conversational telephone speech.

Data

The English data was partitioned into training, development and test sets. The documents were split into sentences and each event mention was assigned to its sentence. Source sentences and their annotations were translated into Brazilian Portuguese using Google Translate and into European Portuguese using DeepL Translate. An alignment algorithm and a parallel corpus word aligner were used to handle mismatches between translated annotations and their translated sentences.

Files are presented in JSON format.

Samples

Please view these samples (JSON): Portuguese (Brazil) and Portuguese (Portugal).

Updates

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