Improving topic modeling through homophily for legal documents

Abstract

Topic modeling that can automatically assign topics to legal documents is very important in the domain of computational law. The relevance of the modeled topics strongly depends on the legal context they are used in. On the other hand, references to laws and prior cases are key elements for judges to rule on a case. Taken together, these references form a network, whose structure can be analysed with network analysis. However, the content of the referenced documents may not be always accessed. Even in that case, the reference structure itself shows that documents share latent similar characteristics. We propose to use this latent structure to improve topic modeling of law cases using document homophily. In this paper, we explore the use of homophily networks extracted from two types of references: prior cases and statute laws, to enhance topic modeling on legal case documents. We conduct in detail, an analysis on a dataset consisting of rich legal cases, i.e., the COLIEE dataset, to create these networks. The homophily networks consist of nodes for legal cases, and edges with weights for the two families of references between the case nodes. We further propose models to use the edge weights for topic modeling. In particular, we propose a cutting model and a weighting model to improve the relational topic model (RTM). The cutting model uses edges with weights higher than a threshold as document links in RTM; the weighting model uses the edge weights to weight the link probability function in RTM. The weights can be obtained either from the co-citations or from the cosine similarity based on an embedding of the homophily networks. Experiments show that the use of the homophily networks for topic modeling significantly outperforms previous studies, and the weighting model is more effective than the cutting model.

Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

“…e length of the shortest element sentence is 30-40 words, and the length of the longest element sentence will reach more than 300 words. e traditional model mostly uses fixed parameters as the vector dimension and fills the vector with 0 for the short sentence, which cannot effectively capture the characteristic representation of sentences with different lengths [5]. In order to weaken the negative impact of the length difference of different sentences on the effect of the model, the multihead self-attention mechanism (MAT) based on the mask method is further integrated into the BERT-CNN model.…”

Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers

“…In this matrix, the measure scale goes from [-1, 1], with 1 denoting the strongest connotation. The work of [26] defines topic variety as the proportion of unique words across all themes. The scale goes from [0, 1], with 0 denoting superfluous topics and 1 denoting topics with more variety.…”

Probit Regressive Tversky Indexed Rocchio Convolutive Deep Neural Learning for Legal Document Data Analytics

“…The designed model reduces the computational complexity but the performance of long documents classification was not improved. Improving topic modeling analysis method was introduced in [16] for legal case documents classification. But it failed to use a multilayer network model for legal case documents categorization.…”