D 2 GCLF : Document-to-Graph Classifier for Legal Document Classification (2022)

Abstract: Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by reasons and facts instead of topics. We propose a Document-to-Graph Classifier (D2GCLF), which extracts facts as relations between key participants in the law case and represents a legal document with four relation graphs. Each graph is responsible for capturing different relations between the litigation participants. We further develop a graph attention network on top of the four relation graphs to classify the legal documents. Experiments on a real-world legal document dataset show that D2GCLF outperforms the state-of-the-art methods in terms of accuracy.

## introduction legal artificial intelligence (legalai) @xcite is a specific subject to apply the artificial intelligence technology into legal tasks including legal judgement prediction @xcite , similar case matching @xcite and law case classification @xcite @xcite . in this paper, we focus on law case classification. the traditional case filing process requires experts to categorize the civil complaints manually, which is labor-intensive. for example, a court in a small town has more than 10,000 civil complaint cases per year in china. this calls for accurate machine learning techniques to improve the efficiency of the case filing process. recently, deep learning approaches @xcite @xcite @xcite have significantly improved the accuracy of text classification on well-known datasets, e.g., amazon reviews. existing methods learn a latent representations for each document by considering the semantics and themes of the documents. in this way, documents are classified into high-level topics such as "sport" and "medical". unlike traditional datasets, legal documents of different types exhibit high semantic similarity. for example, given the nature of legal classification tasks, there are two limitations of applying existing text classification models. first, document structure is a key to accurate classification, but it is ignored in most existing methods. legal documents in different categories may only differ in some particular parts. some previous research proposed to utilize the graph with sentence relations to address the problem @xcite . but not all sentences are relevant to the document class. second, the facts and reasons in the legal cases are essential for distinguishing different dispute types, but they are not considered in existing methods. existing word co-occurrence graphs @xcite @xcite are not only hard to represent the key facts but also include words that are irrelevant to the document class. we argue that the key to accurate legal document classification is to understand the facts, which are expressed as relations between entities. for example, the debtor-creditor relationship denotes the fact that someone borrows other entities' money. motivated by this, we designed a document-to-graph classifier (d2gclf) to address the two limitations above. d2gclf extracts four graphs from each legal document to represent the facts about the key entities, i.e., plaintiffs and defendants. the four graphs are (1) entity-matter graph to describe the matters (e.g., money) associated with the entities; (2) entity-action graph to describe the actions of entities; (3) entity-keyword graph to model the general topic of the facts about ## conclusion this study explores a novel idea of classifying legal documents based on the facts discussed in the documents. we propose a document-to-graph classifier (d2gclf) to implement our idea by modeling the facts as four relation graphs, and applying graph attention network to learn the document representation for classification. experiments on a real-world legal document dataset show the effectiveness of the four relation graphs and the superior performance (91.33% auc) of d2gclf.