Automating code documentation through explanatory text can prove highly beneficial in code understanding.
Summarization of legal case judgement documents is a challenging problem in Legal NLP.
Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts' opinion when compared to the existing methods for network-based legal document similarity.
Automatic summarization of legal case documents is an important and practical challenge.
We propose to augment the PCNet with the hierarchy of legal statutes, to form a heterogeneous network Hier-SPCNet, having citation links between case documents and statutes, as well as citation and hierarchy links among the statutes.
Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval.
Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on.
In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve.
In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language.