Machine Learning Algorithms for Portuguese Named Entity Recognition
INTELIGENCIA ARTIFICIALPublished 18 December 2007Open access
Ruy Luiz Milidiú, Júlio César Duarte, Renato L. G. Cavalcante
Citations23
SJR quartileQ4
SJR score0.25
SNIP0.56
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The results suggest that Machine Learning can be useful in Portuguese NER and indicate that HMM, TBL and SVM perform well in this natural language processing task.
Abstract
"Named Entity Recognition (NER) is an important task in Natural Language Processing. It provides keyfeatures that help on more elaborated document management and information extraction tasks. In thispaper, we propose seven machine learning approaches that use HMM, TBL and SVM to solve PortugueseNER. The performance of each modeling approach is empirically evaluated. The SVM-based extractor showsa 88:11% F-score, which is our best observed value, slightly better than TBL. This is very competitive whencompared to state-of-the-art extractors for similar Portuguese NER problems. Our HMM has reasonableprecision and accuracy and does not require any expert knowledge. This is an advantage for our HMM overthe other approaches. The experimental results suggest that Machine Learning can be useful in PortugueseNER. They also indicate that HMM, TBL and SVM perform well in this natural language processing task"
Keywords
Computer Science
ACM Transactions on Intelligent Systems and TechnologyLIBSVM
41,340 Citations2011Chih-Chung Chang, Chih‐Jen Lin
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
TechnometricsStatistical Learning Theory
26,913 Citations1999Yuhai Wu, Vladimir Vapnik
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings of the IEEEA tutorial on hidden Markov models and selected applications in speech recognition
22,785 Citations1989L. R. Rabiner
Data Mining and Knowledge DiscoveryA Tutorial on Support Vector Machines for Pattern Recognition
16,433 Citations1998Christopher J. C. Burges
There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Proceedings of the IEEEThe viterbi algorithm
5,595 Citations1973G. David Forney
This paper gives a tutorial exposition of the Viterbi algorithm and of how it is implemented and analyzed, and increasing use of the algorithm in a widening variety of areas is foreseen.
IEEE ExpertData mining and knowledge discovery: making sense out of data
4,643 Citations1996U.M. Feyyad
Find loads of the data mining and knowledge discovery making sense out of data book catalogues in this site as the choice of you visiting this page.
Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging
1,531 Citations1995Eric Brill
This paper describes a simple rule-based approach to automated learning of linguistic knowledge that has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance.
A simple rule-based part of speech tagger
1,418 Citations1992Eric Brill
This work presents a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers, demonstrating that the stochastics method is not the only viable method for part ofspeech tagging.
Journal of Artificial Intelligence ResearchActive Learning with Statistical Models
1,286 Citations1996David Cohn, Zoubin Ghahramani +1 more
This work shows how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression.
Named entity recognition using an HMM-based chunk tagger
718 Citations2001Guodong Zhou, Jian Su
A Hidden Markov Model and an HMM-based chunk tagger is proposed, from which a named entity (NE) recognition system is built to recognize and classify names, times and numerical quantities, and the NER problem can be resolved effectively.
Lecture notes in computer scienceREPENTINO – A Wide-Scope Gazetteer for Entity Recognition in Portuguese
22 Citations2006Luís Sarmento, Ana Sofia Pinto +1 more
REPENTINO is a publicly available gazetteer intended to help the development of named entity recognition systems for Portuguese and stores nearly 450k instances of named entities divided in more than 100 categories and subcategories covering a much wider set of domains than those usually included in traditional gazettes.
Lecture notes in computer scienceFunctional Aspects in Portuguese NER
19 Citations2006Eckhard Bick
An improved and modified version of the PALAVRAS-NER parser, adapted for the HAREM joint evaluation task of Named Entity Recognition (NER) in February 2005, is described and evaluated.
Lecture notes in computer scienceA Machine Learning Approach to the Identification of Appositives
13 Citations2006Maria Cláudia de Freitas, Júlio César Duarte +4 more
Experimental results obtained by applying Transformation-based learning (TBL) and Hidden Markov Models (HMM) suggest that a machine learning approach can be beneficial for appositive identification, and also that TBL performs well for this language task.
Lecture notes in computer scienceLearning Named Entity Classifiers Using Support Vector Machines
8 Citations2004Thamar Solorio, Aurelio López López
Support vector machines are used to train a classifier based on the output of an existing extractor system, increasing precision by up to 11.94% and recall by upto 27.83% without considerable human effort.
Lecture notes in computer scienceLearning Named Entity Recognition in Portuguese from Spanish
6 Citations2005Thamar Solorio, Aurelio López López
A practical method for adapting a NER system for Spanish to Portuguese, namely a C4.5, using internal and external features, based on training a machine learning algorithm.
Lecture notes in computer scienceA Complex Evaluation Architecture for HAREM
6 Citations2006Nuno Seco, Diana Santos +2 more
The evaluation architecture and the measures employed in HAREM, the first evaluation contest for named entity recognition in Portuguese, are described.
Lecture notes in computer scienceSemi-supervised Learning for Portuguese Noun Phrase Extraction
5 Citations2006Ruy Luiz Milidiú, Cícero Nogueira dos Santos +2 more
This paper combines Hidden Markov Models and Transformation Based Learning in a semi-supervised learning approach to classify Portuguese noun phrases and shows good improvement with a small labeled corpus and little with a large one.