论文信息 - Document classification using a Bi-LSTM to unclog Brazil's supreme court

Document classification using a Bi-LSTM to unclog Brazil's supreme court

The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to relevant tags and allocated to the right team. Most of the cases reach the court as raster scanned documents with widely variable levels of quality. One of the first steps for the analysis is to classify these documents. In this paper we present a Bidirectional Long Short-Term Memory network (Bi-LSTM) to classify these pieces of legal document.

[1] R. Smith,et al. An Overview of the Tesseract OCR Engine , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[2] Xiaoli Z. Fern,et al. DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference , 2018, NAACL.

[3] Eliyahu Kiperwasser,et al. Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations , 2016, TACL.

[4] Teófilo E. de Campos,et al. LeNER-Br: A Dataset for Named Entity Recognition in Brazilian Legal Text , 2018, PROPOR.

[5] Christopher D. Manning,et al. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[6] Gustavo Carvalho,et al. Document type classification for Brazil’s supreme court using a Convolutional Neural Network , 2018, ICoFCS-2018.

[7] Wei Xu,et al. Bidirectional LSTM-CRF Models for Sequence Tagging , 2015, ArXiv.

[8] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[9] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[10] Un Desa. Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[11] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.

[12] Nemanja Spasojevic,et al. Actionable and Political Text Classification using Word Embeddings and LSTM , 2016, ArXiv.