Intelligent information extraction based on artificial neural network

TL;DR

This paper proposes a modified question answering system utilizing deep neural networks with associative memory to interpret complex questions and retrieve answers from text documents, overcoming limitations of existing systems.

Contribution

It introduces a novel deep neural network architecture with associative memory for enhanced question answering capabilities on complex queries.

Findings

Able to answer complex questions from text documents

Improves upon existing objective-answer-only QAS

Uses deep neural networks with associative memory

Abstract

Question Answering System (QAS) is used for information retrieval and natural language processing (NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but they all are limited to providing objective answers and process simple questions only. Complex questions cannot be answered by the existing QAS, as they require interpretation of the current and old data as well as the question asked by the user. The above limitations can be overcome by using deep cases and neural network. Hence we propose a modified QAS in which we create a deep artificial neural network with associative memory from text documents. The modified QAS processes the contents of the text document provided to it and find the answer to even complex questions in the documents.