An end-to-end joint model for evidence information extraction from court record document

Abstract Information extraction is one of the important tasks in the field of Natural Language Processing (NLP). Most of the existing methods focus on general texts and little attention is paid to information extraction in specialized domains such as legal texts. This paper explores the task of information extraction in the legal field, which aims to extract evidence information from court record documents (CRDs). In the general domain, entities and relations are mostly words and phrases, indicating that they do not span multiple sentences. In contrast, evidence information in CRDs may span multiple sentences, while existing models cannot handle this situation. To address this issue, we first add a classification task in addition to the extraction task. We then formulate the two tasks as a multi-task learning problem and present a novel end-to-end model to jointly address the two tasks. The joint model adopts a shared encoder followed by separate decoders for the two tasks. The experimental results on the dataset show the effectiveness of the proposed model, which can obtain 72.36% F1 score, outperforming previous methods and strong baselines by a large margin.

[1]  Xiaoyong Du,et al.  Analogical Reasoning on Chinese Morphological and Semantic Relations , 2018, ACL.

[2]  Horacio Saggion,et al.  A text summarization method based on fuzzy rules and applicable to automated assessment , 2019, Expert Syst. Appl..

[3]  Grigorios Tsoumakas,et al.  Local word vectors guiding keyphrase extraction , 2018, Inf. Process. Manag..

[4]  Zhen-Hua Ling,et al.  Hybrid semi-Markov CRF for Neural Sequence Labeling , 2018, ACL.

[5]  P. Santhi Thilagam,et al.  Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers , 2019, Inf. Process. Manag..

[6]  Hua Yuan,et al.  On detecting business event from the headlines and leads of massive online news articles , 2019, Inf. Process. Manag..

[7]  Eduard H. Hovy,et al.  End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF , 2016, ACL.

[8]  Han Ren,et al.  Neural Networks for Bacterial Named Entity Recognition , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[9]  James Hammerton,et al.  Named Entity Recognition with Long Short-Term Memory , 2003, CoNLL.

[10]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[11]  Ruihong Huang,et al.  CaseSummarizer: A System for Automated Summarization of Legal Texts , 2016, COLING.

[12]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[13]  Jürgen Schmidhuber,et al.  LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.

[14]  Xinyan Xiao,et al.  Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling , 2019, AAAI.

[15]  Carlos Bobed,et al.  The AIS Project: Boosting Information Extraction from Legal Documents by using Ontologies , 2016, ICAART.

[16]  Imran Sarwar Bajwa,et al.  A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents , 2017, J. Comput..

[17]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[18]  Cícero Nogueira dos Santos,et al.  Boosting Named Entity Recognition with Neural Character Embeddings , 2015, NEWS@ACL.

[19]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[20]  Arunprasath Shankar,et al.  Deep Ensemble Learning for Legal Query Understanding , 2018, CIKM Workshops.

[21]  Philip S. Yu,et al.  Multi-grained Named Entity Recognition , 2019, ACL.

[22]  Wolfgang Alschner,et al.  Towards an automated production of legal texts using recurrent neural networks , 2017, ICAIL.

[23]  Yinglong Ma,et al.  Combining Domain Knowledge Extraction With Graph Long Short-Term Memory for Learning Classification of Chinese Legal Documents , 2019, IEEE Access.

[24]  Yue Zhang,et al.  Hierarchically-Refined Label Attention Network for Sequence Labeling , 2019, EMNLP.

[25]  Donghong Ji,et al.  Dispatched attention with multi-task learning for nested mention recognition , 2020, Inf. Sci..

[26]  Dipankar Das,et al.  Changing Views: Persuasion Modeling and Argument Extraction from Online Discussions , 2019, Inf. Process. Manag..

[27]  Yue Zhang,et al.  NCRF++: An Open-source Neural Sequence Labeling Toolkit , 2018, ACL.

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

[29]  Sukomal Pal,et al.  Text summarization from legal documents: a survey , 2019, Artificial Intelligence Review.

[30]  Ion Androutsopoulos,et al.  Neural Legal Judgment Prediction in English , 2019, ACL.

[31]  Danushka Bollegala,et al.  CLIEL: context-based information extraction from commercial law documents , 2017, ICAIL.

[32]  B. L. William Wong,et al.  An interactive human centered data science approach towards crime pattern analysis , 2019, Inf. Process. Manag..

[33]  Hongli Zhang,et al.  MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction , 2019, IEEE Access.

[34]  Arunprasath Shankar,et al.  Legal Query Reformulation using Deep Learning , 2019, ASAIL@ICAIL.

[35]  Sachin Kumar,et al.  Understanding User Query Intent and Target Terms in Legal Domain , 2019, NLDB.

[36]  Akira Shimazu,et al.  Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts , 2018, Artificial Intelligence and Law.

[37]  Arunprasath Shankar,et al.  Neural Attention Learning for Legal Query Reformulation , 2019, ICAIL.

[38]  Georg Rehm,et al.  Fine-Grained Named Entity Recognition in Legal Documents , 2019, SEMANTiCS.

[39]  Chandra Bhagavatula,et al.  Semi-supervised sequence tagging with bidirectional language models , 2017, ACL.

[40]  Ion Androutsopoulos,et al.  Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation , 2019, Proceedings of the Natural Legal Language Processing Workshop 2019.

[41]  Florian Matthes,et al.  Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification , 2018, AICCC '18.

[42]  Dan Roth,et al.  Design Challenges and Misconceptions in Named Entity Recognition , 2009, CoNLL.

[43]  Yue Zhang,et al.  Chinese NER Using Lattice LSTM , 2018, ACL.

[44]  K. Branting,et al.  Semi-Supervised Methods for Explainable Legal Prediction , 2019, ICAIL.

[45]  Yafeng Ren,et al.  A Hybrid Method to Extract Clinical Information From Chinese Electronic Medical Records , 2019, IEEE Access.

[46]  Kaiz Merchant,et al.  NLP Based Latent Semantic Analysis for Legal Text Summarization , 2018, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[47]  Mari Ostendorf,et al.  Scientific Information Extraction with Semi-supervised Neural Tagging , 2017, EMNLP.

[48]  Peng Zhou,et al.  Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme , 2017, ACL.

[49]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[50]  Massimiliano Giacalone,et al.  Big Data and forensics: An innovative approach for a predictable jurisprudence , 2018, Inf. Sci..

[51]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[52]  Martín Pérez-Pérez,et al.  Online visibility of software-related web sites: The case of biomedical text mining tools , 2019, Inf. Process. Manag..

[53]  Valentin Barrière,et al.  May I Check Again? - A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts , 2019, NODALIDA.

[54]  Donghong Ji,et al.  Recognizing Nested Named Entity in Biomedical Texts: A Neural Network Model with Multi-Task Learning , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[55]  Zhiyuan Liu,et al.  Legal Judgment Prediction via Topological Learning , 2018, EMNLP.