Hierarchical C hinese Legal event extraction via Pedal Attention Mechanism (2020)

Abstract: Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.

## introduction the number of available chinese legal documents has increased dramatically in recent years. event extraction (ee) of legal documents plays an important role in various legal applications, including case push and auxiliary judgment @xcite . for example, a crime-related event contains the defendant's crime facts and crime details, which are key elements to the court's decision. traditional event extraction follows the event structure defined by ace (automatic context extraction) 1 and is divided into two subtasks: (1) event detection, extracting event trigger words in text and predicting event types; (2) event argument extraction, extracting arguments related to events and predicting the roles of the arguments. with the development of natural language processing technology, there are many excellent event extraction techniques in the open field @xcite . there are few event extraction methods for the legal documents. @xcite use a rule-based method to extract event in legal documents. @xcite apply the method of the neural network to the extraction of legal events. however, there are two major issues in legal event extraction that require more effort. on the one hand, the traditional event structure and event definition cannot represent legal events well. for example, in figure 1 traditional event structure cannot express some connections between arguments. if age is used as an argument of event, it will cause ambiguity. besides, according to the traditional event definition method, died event will be separated from stabbed event so that the causal relationship between the victim's death and the defendant's behavior cannot be reflected. to solve this problem, we proposed a dynamic hierarchical event structure to represent legal events according to legal requirements. we first define the hierarchical event types to reflect the inclusion of different legal events. then we carefully design different argument types to include the key elements of legal events. moreover, we propose a dynamic event structure, which stipulates that an event's argument can have child-arguments related to the event. as shown in figure 1 , our event structure can contain all key elements in one event without ambiguity. we extend the event argument extraction task in predicting event-argument roles and argument-argument roles at the same time. furthermore, in order to distinguish between similar events, we add hierarchical event features in event detection. on the other hand, there are long-distance semantic dependence and anaphora resolution problems in the legal sentence. in figure 1 , the first pei is far from died, and in dependency syntax tree, died is related to the second pei. traditional methods like gnn @xcite cannot capture the association between the two words because the two words are far away in the dependency syntax tree. so, we propose a novel pedal attention mechanism to solve this problem. we analyze the attention weights between a word and the dependency adjacent words of another word to determine the semantic relation between the two words. and the dependency adjacent words are called pedal. specifically, this paper proposes a pedal attention based joint hierarchical event extraction model for the legal event. in the training and prediction phase, we design a joint inference model to constrain the overall event. we label a chinese legal event dataset and evaluate our model on it. experiments show that our method outperforms previous state-of-the-art approaches on chinese legal event extraction. moreover, the dynamic event structure we defined for event extraction has important practical significance. the rest of the paper is organized as follows. section 2 introduces the related work of event extraction in the legal domain. section 3 introduces the dynamic hierarchical event structure for the legal event. section 4 details our event extraction model and the joint inference model. in section 5, we describe our experimental setup and discuss the results of dynamic event extraction. we conclude in section 6 with some ideas for future works. 2 related work ## dynamic hierarchical event structure for legal event in this section, we introduce the definition of our legal dynamic hierarchical event (dhe). we first define the legal event as follows: arguments are the elements in the legal event (e.g., parties, locations). we expand the hierarchy argument structure @xcite to cover all elements in the legal event. next, we should define an event structure to represent the relation between events and arguments. the traditional approach is to assign a role to each argument related to the event. however, there are close links between many arguments in legal events. as shown in figure 1 , there is a one-to-one correspondence between ages and parties. however, traditional event structure has no way to reflect this correspondence. in the case of figure 1 , traditional event structure leads to ambiguity. in fact, in linguistics, a dynamic event structure theory @xcite is proposed for this situation. in order to accurately retain the key elements in the legal event, we design a dynamic event structure to represent legal events. ## pedal attention based joint hierarchical event extraction model in this section, we introduce a novel extraction model for dynamic event structure named pedal attention based joint hierarchical event extraction (pajhee). traditional event structure is a special case of dynamic event structure, so pajhee also suitable for the extraction of traditional event structure. the overall architecture of pajhee is shown in figure 3 . for a given sentence @xmath0, x 2 , . . . , x n with lenght n, where x i is the i-th token, pajhee extract the dynamic event in s. our model consists of the following modules: (1) candidate argument extractor that extracts candidate argument mentions @xmath1, a 2 , . . . , a k and the correspond argument type a @xmath2, a t 2 , . . . , a t k from s, (2) hierarchical event feature construction module that constructs the hierarchical event type features of s, (3) pedal attention mechanism that extracts the semantic relationship between the arguments and the triggers, (4) trigger extraction module that predicts the labels of all candidate triggers foot_0 , (5) argument role prediction module that predicts the event-argument roles and the argumentargument roles in dynamic event structure, (6) joint inference model that generates the joint probability for training and extraction. ## conclusion and future work this paper analyzes the shortcomings of the traditional event structure in the legal field and defines a dynamic hierarchical event structure that can better express event information. we label a legal event extraction dataset that contains 11 types of events in two major legal-related events: crime and lit-igation. then we propose a pedal attention based joint hierarchical event extraction model, which can automatically extract lexical level and event-level features from plain texts. a novel pedal attention mechanism is introduced to capture lexical level semantic relation, and a hierarchical event feature is used to construct the event level feature. the experimental results prove the effectiveness of the proposed method. in the future, we will extend the dynamic event structure to other fields such as finance and biomedicine and apply the pedal attention mechanism in other natural language processing tasks as a general relational feature construction tool.