Abstract:
The judicial system is the central pillar of law and order across the world. It is re-
sponsible for maintaining order amongst citizens and also solving litigations that arise.
Although this system has worked quite well, there still exists several challenges, such as
racial biases in cases, shortage of legal professionals and inconsistencies with regards to
rulings in cases. These challenges need to be addressed in order to maintain law and
order in society and to help strengthen the criminal justice system.
Researchers have incorporated Natural Language Processing (NLP) techniques to help
address some of these challenges. Focusing primarily on three legal applications, which
are Legal Judgment Prediction (LJP), Similar Case Matching (SCM) and Legal Question
Answering (LQA)[28].
SCM focuses on identifying the relationships among cases using the available informa-
tion. In other words, SCM is focused on segmenting or grouping legal cases. This is
especially useful for Common Law judicial systems, where judicial decisions are based
on similar and representative cases that have happened in the past. South Africa uses
this type of judicial system.
Although good progress has been made in SCM applications, there currently exists sev-
eral challenges found in the these models. These challenges include using entities found
in a legal document to improve the matching of similar cases and the interpretability of
these models.
In this research we will focus on applying the SCM application on South African criminal
cases, by creating a model that will be able to match similar crime cases together. This
model will also solve the two challenges currently faced in SCM applications.
We found that using a Named Entity Recognizer (NER) with a Paragraph Vector-
Distributed memory (PV-DM) model produced better results than using conventional
PV-DM or TFIDF model. This model also overcomes the current SCM challenges as
it uses the entities found in cases as the main variables for the model (using the NER
model). Since the entities help explain how the model mapped similar case, this makes
the model also interpretable.
Based on the accuracy (similarity score) of the model, we can use this model as tool to
segment criminal cases in real life.
