Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support

Abstract

Assessing risk for voluminous legal documents such as request for proposal, contracts is tedious and error prone. We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document. Our framework uses Paragraph Vector, an unsupervised model to generate vector representation of text. This enables the framework to learn contextual relations of legal terms and generate sensible context aware embedding. The framework then feeds the vector space into a supervised classification algorithm to predict whether a paragraph belongs to a pre-defined risk category or not. The framework thus extracts risk prone paragraphs. This technique efficiently overcomes the limitations of keyword based search. We have achieved an accuracy of 91% for the risk category having the largest training dataset. This framework will help organizations optimize effort to identify risk from large document base with minimal human intervention and thus will help to have risk mitigated sustainable growth. Its machine learning capability makes it scalable to uncover relevant information from any type of document apart from legal documents, provided the library is pre-populated and rich.

Effective and scalable legal judgment recommendation using pre-learned word embedding

“…Sagathadasa et al [30] employed Word2Vec and lexical relevance to capture domain-specific semantic similarity. Dipankar et al [2] designed a "risk o meter" framework to analyze the risk associated with the Legal contracts using supervised machine learning and Doc2Vec. In the empirical analysis of Mandal et al [20], Doc2vec has superior performance in terms of Accuracy and Correlation referenced to the human expert similarity score, compared to TF-IDF, LDA, and Word2Vec.…”

“…The Apache Hadoop software library is a framework for the distributed processing: https://hadoop.apache.org/ 2. Apache Spark is a framework for the in-memory distributed processing: https://spark.apache.org/.…”

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

“…The unsupervised learning algorithms use input documents to find structure or pattern by observing the association between the input documents. However, very few studies used this type of learning [3], [19], [37], [63]. 3) Feature-inferring Neural Network approaches:The third category, feature-inferring Neural Network approaches, differs from other two approaches in utilizing and extracting features from DL models.…”