A Preliminary Approach to the Multilabel Classification Problem of Portuguese Juridical Documents

Gonçalves, Teresa; Quaresma, Paulo

doi:10.1007/978-3-540-24580-3_50

Cited by 46 publications

(26 citation statements)

References 4 publications

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“…Binary relevance [9], a straightforward problem transformation approach, predicts positively the label sets of an unknown instance by N binary classifiers. Independent classifiers are commonly individual selection [10,11] and fusing selection [12]. This one-against-all strategy has been criticized to ignore the correlations among labels [13].…”

Section: Introductionmentioning

confidence: 99%

REAL: Multi-Label Learning with Relevant Feature for Each Label

Yan

Guo

2017

ijecs

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Abstract:Multi-label learning problems have become a key topic in machine learning research in recent years. However, most approaches have focused on exploiting the interdependences between labels, whereas the correlations between the original features and each group of possible class labels have been rarely examined. The association degree of a selected feature is biased toward each discriminate class label. With the aim of addressing the gaps in previous studies, the current paper proposes a novel framework called multi-label learning with Relevant fEature for eAch Label. Using this mechanism, a classification model to deal with enron and medical data sets is established. The experimental results demonstrate the effectiveness and competitive performance of the proposed scheme which outperformed other multi-label classification methods significantly.

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Section: Introductionmentioning

confidence: 99%

REAL: Multi-Label Learning with Relevant Feature for Each Label

Yan

Guo

2017

ijecs

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“…In [6], SVM performance is compared with other Machine Learning algorithms and in [7] a thorough study on some preprocessing techniques (feature reduction, feature subset selection and term weighting) is made over European Portuguese and English datasets. The impact of using linguistic information on the preprocessing phase is reported in [15] over a Brazilian dataset.…”

Section: Introductionmentioning

confidence: 99%

Analysing Part-of-Speech for Portuguese Text Classification

Gonçalves

Silva

Quaresma

et al. 2006

Computational Linguistics and Intelligent Text Processing

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Abstract. This paper proposes and evaluates the use of linguistic information in the pre-processing phase of text classification. We present several experiments evaluating the selection of terms based on different measures and linguistic knowledge. To build the classifier we used Support Vector Machines (SVM), which are known to produce good results on text classification tasks. Our proposals were applied to two different datasets written in the Portuguese language: articles from a Brazilian newspaper (Folha de São Paulo) and juridical documents from the Portuguese Attorney General's Office. The results show the relevance of part-of-speech information for the pre-processing phase of text classification allowing for a strong reduction of the number of features needed in the text classification.

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“…On previous work, we evaluated SVM performance compared with other Machine Learning algorithms [2] and performed a preliminary study on the impact of using linguistic information to reduce the number of features [3]. In this paper, we extend that work using IR techniques to weight and normalise features.…”

Section: Introductionmentioning

confidence: 99%

Using IR Techniques to Improve Automated Text Classification

Gonçalves

Quaresma

2004

Natural Language Processing and Information Systems

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Abstract. This paper performs a study on the pre-processing phase of the automated text classification problem. We use the linear Support Vector Machine paradigm applied to datasets written in the English and the European Portuguese languages -the Reuters and the Portuguese Attorney General's Office datasets, respectively. The study can be seen as a search, for the best document representation, in three different axes: the feature reduction (using linguistic information), the feature selection (using word frequencies) and the term weighting (using information retrieval measures).

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A Preliminary Approach to the Multilabel Classification Problem of Portuguese Juridical Documents

Cited by 46 publications

References 4 publications

REAL: Multi-Label Learning with Relevant Feature for Each Label

REAL: Multi-Label Learning with Relevant Feature for Each Label

Analysing Part-of-Speech for Portuguese Text Classification

Using IR Techniques to Improve Automated Text Classification

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