Concatenating Top2Vec and Node2Vec embeddings, where the Node2Vec graph encodes Top2Vec's own topic labels, yields compact clusters, but the gain is largely circular.
Methods for Computing Legal Document Similarity: A Comparative Study
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abstract
Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval. Finding similar legal documents has many applications in downstream tasks, including prior-case retrieval, recommendation of legal articles, and so on. Prior works have proposed two broad ways of measuring similarity between legal documents - analyzing the precedent citation network, and measuring similarity based on textual content similarity measures. But there has not been a comprehensive comparison of these existing methods on a common platform. In this paper, we perform the first systematic analysis of the existing methods. In addition, we explore two promising new similarity computation methods - one text-based and the other based on network embeddings, which have not been considered till now.
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Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering
Concatenating Top2Vec and Node2Vec embeddings, where the Node2Vec graph encodes Top2Vec's own topic labels, yields compact clusters, but the gain is largely circular.