Emmanouil Fergadiotis
2019
Large-Scale Multi-Label Text Classification on EU Legislation
Ilias Chalkidis
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Emmanouil Fergadiotis
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Prodromos Malakasiotis
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Ion Androutsopoulos
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EUR-LEX, annotated with ∼4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT’s maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.
Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation
Ilias Chalkidis
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Emmanouil Fergadiotis
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Prodromos Malakasiotis
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Nikolaos Aletras
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Ion Androutsopoulos
Proceedings of the Natural Legal Language Processing Workshop 2019
We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union’s public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.
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