Keywords based Closed Domain Question Answering System for Indian Penal Code Sections and Indian Amendment Laws

Abstract

In information retrieval, Question Answering (QA) is the task of answering a question posed in natural language (NL) using either a pre-structured database or a collection of natural language documents without human intervention. Question Answering systems are categorized on their available resource for answers. The domain specific Question Answering System gives more exact and correct answers than web based Question Answering system as it is limited for only one domain resource to answer. This paper proposes the closed domain Question Answering System for handling the legal documents of Indian Penal Code (IPC) sections and Indian Amendment Laws to retrieve more precise answers. This system tries to retrieve the exact answers from stored knowledge-base for the query related to Indian Penal Code (IPC) sections and Indian Amendment Laws asked by user. This Keyword based Question Answering System works on structured, unstructured and non-question form queries. The closed domain Question Answering system gives more accurate answer than other open domain system as it restricted single resource. Keywords from both queries and answer corpus play important role for extracting answer.

Text similarity algorithms to determine Indian penal code sections for offence report

“…This QAS select keywords and question type from query and response according answer stored in corpus. Authors define that problem lies on intersection of two domains: Information retrieval (IR) and natural language processing (NLP) [7]. Sangeetha et al have proposed an information retrieval system is designed to retrieve relevant answers about laws.…”

Ambiguity in Question Paper Translation

“…In 2015, Kamdi and Agrawal [37] give the concept of question answering system for Indian Penal code section and Indian amendment laws by using keywords based closed domain. In this work authors also defined the process of the question in three types as determining the type of question, determining the type of answer and extracting keywords from the question and formulate a query.…”

An Efficient Approach for Keyphrase Extraction from English Document

“…Many research articles have been found in the literature for keyphrase extraction used machine learning or data mining approaches [23][24][25][26][27][28][29][30][31]. A Naive Bayes approach is used for keyword generation [23].…”