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Mechanism to Generate Mock Test Questions Based on Unstructured Text Documents

IP.com Disclosure Number: IPCOM000250152D
Publication Date: 2017-Jun-06
Document File: 2 page(s) / 89K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a mechanism and system that use text analytics to analyze unstructured

electronic text and then generate mock test questions to assist a user in studying specific

material.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

1

Mechanism to Generate Mock Test Questions Based on Unstructured Text Documents

Abstract

Disclosed are a mechanism and system that use text analytics to analyze unstructured

electronic text and then generate mock test questions to assist a user in studying specific

material.

When a person is reading a textbook, it is often difficult to determine the material on which to focus to prepare for tests. In addition, practice exams might not be available for a person to take in order to gauge which areas of the material require further study. Further, a user might want a way to memorize/learn a subject matter from electronic text (e.g., article, information material, books, etc.). As the number of online textbooks or knowledge websites increases, it would be helpful to automatically generate mock tests for users. Test training and assistance companies, as well as educational institutions, could utilize such software to improve services for customers and students. Companies across industries could utilize this technology to help train employees on subject matter material.

The novel solution is to analyze unstructured electronic text (e.g., (ebooks) to generate mock test questions using text analytics (rules based + statistical)). The novel system uses analytics dictionaries and parsing rules for analysis of rules-based text. Text analytics helps identify data such as dates, measurements, topic, body parts, drugs, etc.

For example, the system can analyze a medical book for human anatomy information. Text in the book might say, "foot has 26 bones". Using text analytics, the system can determine that “foot” is a body part and it has 26 bones. The novel system can then generate a mock question asking how many bones a foot has. It is known that 26 is a number and it is a quantity....