Legal NERC with ontologies, Wikipedia and curriculum learning

Abstract

In this paper, we present a Wikipediabased approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al., 2007), and a Wikipediabased ontology, YAGO (Suchanek et al., 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation.We resort to a technique called curriculum learning aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, we find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

“…Several studies (Chalkidis et al, , 2019cHendrycks et al, 2021) explored information extraction from contracts, to extract important information such as the contracting parties, agreed payment amount, start and end dates, applicable law, etc. Other studies focus on extracting information from legislation (Cardellino et al, 2017;Angelidis et al, 2018) or court cases (Leitner et al, 2019).…”

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

“…IE in LegalAI has also attracted the interests of many researchers. To make better use of the particularity of legal texts, researchers try to use ontology (Bruckschen et al, 2010;Cardellino et al, 2017;Lenci et al, 2009; or global consistency (Yin et al, 2018) for named entity recognition in LegalAI. To extract relationship and events from legal documents, re-searchers attempt to apply different NLP technologies, including hand-crafted rules (Bartolini et al, 2004;Truyens and Eecke, 2014), CRF (Vacek and Schilder, 2017), joint models like SVM, CNN, GRU (Vacek et al, 2019), or scale-free identifier network (Yan et al, 2017) for promising results.…”

“…Several new LegalAI datasets have been proposed (Kano et al, 2018;Duan et al, 2019;Chalkidis et al, 2019b,a), which can serve as benchmarks for research in the field. Based on these datasets, researchers began exploring NLP-based solutions to a variety of LegalAI tasks, such as Legal Judgment Prediction (Aletras et al, 2016;Luo et al, 2017;Chen et al, 2019), Court View Generation (Ye et al, 2018), Legal Entity Recognition and Classification (Cardellino et al, 2017;ANGELIDIS et al, 2018), Legal Question Answering (Monroy et al, 2009;Taniguchi and Kano, 2016;Kim and Goebel, 2017), Legal Summarization (Hachey and Grover, 2006;Bhattacharya et al, 2019).…”

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

“…Some popular neural network methods are used in an automatic charge prediction task [22][23][24], and there are some works focusing on identifying applicable law articles for a given case [25][26][27]. In addition, some researchers focus on other areas of justice such as entity recognition [28,29], court opinion generation [30] and analysis [31].…”