Question Answering System using machine learning approach
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The document discusses the development of a web-based bilingual question answering system using machine learning techniques, focusing on its architecture, implementation, and experimental results. It highlights the advantages of natural language processing in facilitating user interactions and addresses the challenges in generating accurate responses to queries. The system demonstrates a high overall accuracy of 92% and plans for future enhancements including multilingual support.
Question Answering System using machine learning approach
- 1. A WEB BASED BILINGUAL QUESTION ANSWERING SYSTEM USING MACHINE LEARNING APPROACH BY: GARIMA NANDA
- 2. Agenda Introduction Applications Motivation Problem Statement How QA is different from SE Architecture Implementation Experimental Results Conclusion and Future Work References
- 3. Introduction Natural Language Processing (NLP) is a field of computer science and computational linguistics concerned with the Interaction between computer systems and human being. A Question Answering System is fairly an IR system in which a query is stated to the system and it relocates the correct or closest results to the specific query asked in Natural Language. It is one of the consequences of Natural Language Interface to Database (NLIDB). The main aim of QA is to present the user with a short answer to a question rather than a list of possibly relevant documents.
- 4. User interaction with QAS
- 5. Applications Applications of Natural Language Processing: Speech recognition. Artificial intelligence and Expert System. Natural language interface to database. Fields in which QAS is employed: Agriculture: Interface for providing agriculture field related information to the farmers Sports: Can be used at school level for students. Railways: For providing automated information regarding the railways or flights. Some existing QA systems in specific domains- BASEBALL LUNAR START SHRDLU
- 6. Motivation As it becomes more and more difficult to find answers on the WWW using standard search engines, question answering technology will become increasingly important. Greater relevance to find correct answer. Potentially allows everyone to participate in today’s information revolution. Benefit to people not knowing formal query language. Find answer to General Knowledge type of questions. The accuracy of QA system can be found. Finds answers to such types of questions: Who? What? Where? How? and क्या, कब, कौन, कै से etc.
- 7. Problem Statement To design a Graphical User Interface that can generate Bilingual Natural Language output. To design a QAS using General Knowledge Database. To develop a system which automatically predict the misspelled words and jumbled phrases with their actual word related to the query To develop a system which gives correct and concise result related to the questions. Evaluate the performance and accuracy of the overall system. Apply the best similarity measure in the system to handle the queries in many ways.
- 8. How QAS is different from SE QA System Search Engine Query in Natural Language(Question) Queries based on keywords Present answers to users Users find answers from retrieved results Some NL process used to determine results Mostly keywords and ranking to retrieve results
- 10. Architecture contd… Three main phases of Architecture- Along with the Knowledge Base (SWD + Entities + Trained Data). Accessing Natural Query Phase Phase I Feature Extraction Phase Phase II Classification Phase Phase III
- 11. Architecture contd… Identify the nature or type of query. Query statement is read, processed and tokenized. Removal of Bag-of-words from the query, resulting into tokens. Accessing NL Query phase
- 12. Architecture contd… Feature extraction; to extract features (i.e. known keywords are extracted using similarity). Data gets trained here resulting into feature vector Feature Extraction Phase
- 13. Architecture contd… In Classification phase, FV is compared to the Feature Space(i.e Trained Data) resulting in Labels. Labels will be directly represent the answer of the query. Finally in this way the answer is predicted after comparison and result is displayed. Classification Phase
- 14. Implementation Web Based Graphical User Interface has been implemented into the system so as increase its scope and portability. Auto complete feature along with the query input text box has been implemented. Queries with misspelled words and jumbled phrases accepted by the implemented system. The Machine Learning Approach has been used so as to train the system as a result of which the accuracy as well as the efficiency of the system has been increased.
- 15. Implementation contd… The Web based Graphical User Interface for QAS has been shown below with the help of a snapshot. Language Selection Screen
- 16. Implementation contd… Main Screen
- 17. Implementation contd… Question Input Screen in Hindi Language
- 18. Implementation contd… Auto Complete feature has been illustrated below with the help of a snapshot
- 19. Implementation contd… Result of the executed query snapshot is shown below.
- 20. Question Input Screen with Autocomplete feature for query in English Language
- 21. Result screen with specific output to the query in English
- 22. Experimental Results Test Set Total Correct Overall Accuracy % TS1 25 23 92 TS2 50 44 88 TS1, TS2- test sets TS1 carries those questions which are queried by a known user and TS2 are those questions which are given by that user who is unaware about the domain Overall accuracy is computed when a complete test set is given as input to the QA system.
- 23. Experimental Results contd… 86 87 88 89 90 91 92 TS1 TS2 Overall Acc. % Overall Acc. % The accuracy percentage is itself reflecting that which of the two test set is performing better
- 24. CONCLUSION AND FUTURE WORK The Question answering system for Hindi Natural Language gives a vast idea of QA System with Overall accuracy of 92% and threshold of 0.9. The concepts of Overall Accuracy and similarity are used here. This is far better than the concepts being used in earlier systems. The system has been made Web Based and has been provided with the functionality of AutoComplete feature to increase the scope and make it more user friendly respectively. Our system removes the limitation of existing work. Making the system Multilingual by incorporating other languages such as English, French, Spanish languages along with these two languages. Further different Classifiers can be tested on our system
- 25. References Sunil A. Khillare, Bharat A. Shelke, and C. Namrata Mahender, "Comparitive Study on Question Answering Systems and Techniques," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, pp. 775-778, November 2014. Jovita, Linda, Andrei Hartawan, DerwinSuhartono," Using Vector Space Model in Question Answering System,"International Conference on Computer Science and Computational Intelligence (ICCSCI 2015), ScienceDirect, pp. 305-311. Show-Jane Yen, Yu-ChiehWu, Jie-Chi Yang, Yue-Shi Lee, Chung-Jung Lee, Jui-Jung Liu," A support vector machine-based context-ranking model for question answering," Information Sciences, ScienceDirect, pp. 77-87, 2013. Asma Ben Abacha a, Pierre Zweigenbaum," MEANS: A medical questionanswering system combining NLP techniques and semantic Web technologies," Information Processing and Management, ScienceDirect, pp. 570-594, 2015. Sneha Bagde, Mohit Dua, and Zorawar Singh Virk, " Comparison of Different Similarity Functions on Hindi QA System," International conference on ICT for Sustainable Development (ICT4SD), Springer, pp. 657-663, vol. 408, February 2016.
- 26. Smith Mahboob Alam Khalid, Valentin Jijkoun and Maarten de Rijke, “ Machine Learning for Question Answering from Tabular Data, “ 18th International Workshop on Database and Expert Systems Applications, IEEE, 2007. Sanjay K Dwivedi, Vaishali Singh, “Research and Reviews in Question answering system,” International Conference on Computational Intelligence: Modelling Techniques and Applications, ScienceDirect, pp. 417-424, 2013. Er. Amit Chaudhary, Er. Annu Battan," Natural Language Interface to Databases- An Introduction," International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 7, July 2014. Rajender Kumar, Mohit Dua, Shivani Jindal," D-HIRD: DomainIndependent Hindi Language Interface to Relational Database," International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), IEEE, 2014. Mohit Dua, Sandeep Kumar, Zorawar Singh Virk, “ Hindi Language Graphical User Interface to Database Management System,” International Conference on Machine Learning and Applications, IEEE, 2013.
- 27. THANK YOU…