Detection of Catchphrases and Precedence in Legal Documents

Yogesh H. Kulkarni, Rishabh Patil, Srinivasan Shridharan
2017 Forum for Information Retrieval Evaluation  
Common Law System" practiced in India refers to statute as well as precedent to form judgments. As number of cases are increasing rapidly, automation becomes highly desirable. This paper presents two such systems viz. Automatic Catchphrase Detection and Automatic Precedence Detection. Automatic Catchphrase Detection: One of the key requirements of such information retrieval system is to pre-populate database of prior cases with catchphrases for better indexing and faster, relevant retrieval.
more » ... s paper proposes an automatic catchphrases prediction for cases for the same. The problem catchphrase detection has been modeled as "custom named entity recognition (NER) using conditional random fields (CRF)". CRF is trained with pairs of prior cases and their respective catchphrases, the gold standards. The model is, then used to predict catch-phases of unseen legal texts. End of the first section demonstrates efficacy of the proposed system using practical data-set. Automatic Precedence Detection: Due to thousands of past cases it becomes tedious and error-prone to find relevant precedent, manually. An automatic precedent retrieval system is the need of the hour. One of the key requirements of such information system is to find cases which could be "similar" to the case in hand. The "similarity" used in this paper is about citations. The problem is of predicting prior cases which could potentially be cited by a particular case text. This paper proposes such association system using mixed approaches. It employs rule-based Regular Expressions based on references to statute and Articles. It finds cosine similarity between cases using vectors generated by popular word embedding called doc2vec. It also leverages topic modeling by finding matches between cases based on the number of common topic words. End of the second section demonstrates efficacy of the proposed system by generating cite-able documents from test data-set.
dblp:conf/fire/KulkarniPS17 fatcat:vorpos3u3nd5bgovtzeqhgikxa