Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training
暂无分享,去创建一个
Frank Schilder | Dezhao Song | Kanika Madan | Andrew Vold | Dezhao Song | Frank Schilder | A. Vold | Kanika Madan
[1] Frank Schilder,et al. Litigation Analytics: Extracting and querying motions and orders from US federal courts , 2019, NAACL-HLT.
[2] Yukihiro Tagami,et al. AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification , 2017, KDD.
[3] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[4] Pradeep Ravikumar,et al. PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification , 2016, ICML.
[5] Ion Androutsopoulos,et al. Neural Legal Judgment Prediction in English , 2019, ACL.
[6] Jure Leskovec,et al. Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.
[7] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.
[8] Peng Zhou,et al. FastBERT: a Self-distilling BERT with Adaptive Inference Time , 2020, ACL.
[9] Masha Medvedeva,et al. Using machine learning to predict decisions of the European Court of Human Rights , 2019, Artificial Intelligence and Law.
[10] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[11] Arman Cohan,et al. Longformer: The Long-Document Transformer , 2020, ArXiv.
[12] Ramakanth Kavuluru,et al. Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces , 2018, EMNLP.
[13] Yiming Yang,et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.
[14] Ming-Wei Chang,et al. Zero-Shot Entity Linking by Reading Entity Descriptions , 2019, ACL.
[15] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[16] Iz Beltagy,et al. SciBERT: A Pretrained Language Model for Scientific Text , 2019, EMNLP.
[17] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[18] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[19] Lukasz Kaiser,et al. Reformer: The Efficient Transformer , 2020, ICLR.
[20] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[21] Venkatesh Balasubramanian,et al. Slice: Scalable Linear Extreme Classifiers Trained on 100 Million Labels for Related Searches , 2019, WSDM.
[22] Pascale Kuntz,et al. CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning , 2018, ICML.
[23] Ion Androutsopoulos,et al. Large-Scale Multi-Label Text Classification on EU Legislation , 2019, ACL.
[24] Jimeng Sun,et al. Explainable Prediction of Medical Codes from Clinical Text , 2018, NAACL.
[25] Jinbo Bi,et al. Large Scale Diagnostic Code Classification for Medical Patient Records , 2008, IJCNLP.
[26] Brian D. Davison,et al. Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification , 2020, ICML.
[27] Zhiyuan Liu,et al. CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction , 2018, ArXiv.
[28] A. Viera,et al. Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.
[29] A. Zubiaga. Enhancing Navigation on Wikipedia with Social Tags , 2012, ArXiv.
[30] Jiun-Hung Chen,et al. A multi-label classification based approach for sentiment classification , 2015, Expert Syst. Appl..
[31] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[32] Kai Zou,et al. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks , 2019, EMNLP.
[33] Linlin Liu,et al. DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks , 2020, EMNLP.
[34] I. Dhillon,et al. Taming Pretrained Transformers for Extreme Multi-label Text Classification , 2019, KDD.
[35] Manik Varma,et al. FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning , 2014, KDD.
[36] Thomas Wolf,et al. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter , 2019, ArXiv.
[37] Thomas Wolf,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[38] Jaewoo Kang,et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..
[39] Zihan Zhang,et al. AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification , 2019, NeurIPS.
[40] Xin Chen,et al. Mining Social Media Data for Understanding Students’ Learning Experiences , 2014, IEEE Transactions on Learning Technologies.
[41] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[42] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[43] Iryna Gurevych,et al. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , 2019, EMNLP.
[44] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[45] Johannes Fürnkranz,et al. Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain , 2008, ECML/PKDD.