Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description (2020)

Abstract: In this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case. We treat the fact descriptions as narrative texts and the defendants as roles over the narrative story. We propose to model the defendants with behavioral semantic information and statistical characteristics , then learning the importances of defendants within a learning-to-rank framework. Experimental results on a real-world dataset demonstrate the behavior analysis can effectively model the defendants’ impacts in a complex case.

## introduction in recent years, much previous work has focused on the building of legal assistant systems with different functions, e.g. searching relevant cases for a given query @xcite , predicting charge labels based on the fact description in a criminal case @xcite @xcite , generating the interpretable court views from the fact descriptions @xcite . though having achieved promising results in this field, most of the work only studies the simple cases with only one defendant. however, there exist lots of complex criminal cases in practice, which will involve multiple criminals. in this work, we propose to study the identification of principals and accessories from the fact description in a criminal case. the principal refers to a criminal who organizes and leads criminal groups to carry out criminal activities or plays a major role in joint crimes. correspondingly, we refer the accessory as the one who plays a secondary or auxiliary role. as the illustration of our task in fig. 1 , given the fact description as well as a list of defendants, we expect to identify the principals and accessories from the defendants. since the fact descriptions in criminal cases are usually narrative texts which mostly record the criminal events, we treat the defendants in the fact descriptions as the narrative roles and the protagonists who have the greater impact will be identified as the principles. narrative comprehension has been studied in nlp for a long time. the traditional method to measure the importances of roles is based on the roles' dispersion over the story @xcite . it supposes that comparing to less important roles, the roles with bigger impact are expected to appear at more places and are more evenly distributed over the story. however, this assumption ignores actions of roles (denoted as behavioral semantic information), which may be a key factor that estimates their impacts in legalcontext scenarios. in this paper, we propose to model a defendant from two perspectives of behavioral semantic information and statistical characteristics. after that, we further learned to estimate their importances with a learning-to-rank framework @xcite . our contributions in this paper can be summarized as: ## methodology given the fact description f of a case and its defendants set d(d 1 , d 2 , ..., d n ), we expect to classify each d i as either a principal or an accessory. a function f for scoring each defendant is learned by a ranking method and we regard its result as the probability of d i being a principal. note that this could be treated as a classification problem without loss of generality. ## results and discussion tab. 2 presents the performances of different role modeling methods. it can be seen that our model achieves a considerable improvement in p macro , r macro and f macro . as shown in fig. 3 , the defendant in red is the mastermind of the case and should be judged to be the principal, despite his low appearances. position or frequency information does not effectively reflect the status of a role in such samples. however, our method captures this information by the cooperation mode feature between yin and zhao, with the help of verb "instructed". yin instructed zhao to be a drug seller and sold two small packages of methamphetamine to wang peng at the price of 1000 yuan. zhao collected 1000 yuan of poison money. we compare the performances of our two feature families to explore which one contributes more to the task and the result is shown in tab. 4. we find that the feature family f semantic achieve better performance in all the evaluation metrics. and its results are even better than our baselines. it reveals that defendant's behavioral semantic information is more valuable than those statistical characteristics. ## feature selection we expect to find a feature conjunction that makes the most sense for modeling role's impact in a story. like @xcite , we use an advanced greedy method to find the best feature conjunction. given all n (it is 10 in this paper) features we extracted, we construct 2 n feature sets and randomly pick 100 of them. then, we run the greedy selection algorithm based on the feature set (denoted as best) with the best map among those 100 feature sets. features excluded those in best are denoted as ex best and all the extracted features are denoted as full. we evaluate the best and each feature in ex best and if the result is better than the previous one, this feature will be added into the best. we repeat the process until the best is no longer updated. finally, we get the best feature conjunction composed by f semantic, vpdepth, order of appearances, number of occurrences, cooperation mode. to reflect the gap between the best and the full, we evaluate their performances on datasets with different numbers of defendants. tab. 4 illustrates the best feature set also outperforms the full feature set when dealing with cases with different numbers of defendants. model f macro (%) #@xmath0.33 80.62 77.28 best 82.85 81.41 79.54 we are interested in which features in particular are highly valued for role modeling. the importance of each feature is evaluated by the decrease of performance when removing this feature measured from the best. fig. 4 reveals the importance of each feature for role modeling. we observe that f semantic plays a very important role. the f macro declines seriously (more than 6 percentage points) when we remove it from the feature set. we suppose that semantic features represent the behavioral information of roles and a defendant's behavior is of great concern in determining his criminal responsibility. the match result of a defendant's actions and global description of the case can effectively model his influence in the whole case. ## conclusion in this paper, we study the task of identifying principals and accessories from the fact description in a complex case. we find a set of effective features for role modeling. and evaluate that the behavioral semantic information is most worthy of attention. we hope to address this problem with a completely semantic-based approach in the future.