A Novel Model for QUestion Answering Railway System: QUARS

Transcription

1 Indian Journal of Science and Technology, Vol 9(20), DOI: /ijst/2016/v9i20/84292, May 2016 ISSN (Print) : ISSN (Online) : A Novel Model for QUestion Answering Railway System: QUARS Lovely Sharma 1*, Vijay Dhir 2 and Kamaljeet Kaur 3 1 P. G. Department of Computer Science and IT, Doaba College, Jalandhar , Punjab, India; lvlysharma.85@gmail.com 2 St. Soldier Institute of Engineering & Technology, Jalandhar , Punjab, India; drvijaydhir@hotmail.com 3 Department of Computer Science, D.A.V College, Amritsar , Punjab, India; kamaljit_batra@yahoo.com Abstract Objectives: The plethora of information available on the web makes it difficult to search for a specific and suitable query. So, Question Answering dialogue system becomes helpful to provide precise and short answers to users instead of lengthy documents or passages. Method: The main focus is on keyword based approach and partial linguistic analysis to process the query. The main component is Dialogue Manager which clarifies the conversation between user and system to resolve the elliptical and anaphoric problems. Findings/ Application: The paper is based upon the development of Question answering dialogue system in Hindi language for the domain of Railways. The study is restricted to Northern India alone. The existing system of Indian railway does not support Question Answering system while this novel system will provide anytime anywhere facility of enquiry about railway. The implemented system is quite simple, interactive and easy to use such that end user can ask the query using Hindi transliteration. The results are quite satisfactory as precision and recall values are 98% and 97% respectively. Keywords: Dialogue Manager, Partial Syntactic and Semantic Analysis, Query Processing, Question Answering Railway System 1. Introduction QA dialogue systems particularly focus on providing succinct answers to arbitrary queries stated in natural language. It provides only the precise and short answers to users instead of lengthy documents which are generated by search engines. For example, the question is What is the fare for Amritsar Express from Jalandhar to Amritsar? would be preferably answered as 250 Rs. This mechanism is intuitive and save lots of time. Hence, much work has been done on QA system in dissimilar areas like in Biology 1 and Chemistry 2 etc. Although, these practical QA systems are not so much usable in our everyday life. So, we opt for Railway domain of Northern area to resolve the Railway enquires because users often have specific questions in their mind, for which they expect nugget of information in their native language without being restricted to linguistic knowledge of that native language. This could be achieved with the help of QA Dialogue System. The developed QA architecture provides concise and accurate answers to users in the Hindi language. In this paper, we implement QUestion Answering Railways System (QUARS) for Question answering in Hindi language focusing upon the domain of Northern India 1 for handling Railways enquiries. The developed system architecture follows the keyword based approach and partial linguistic analysis of the Natural language query. The QA system must analyse and process the query from a linguistic point of view and understand what the user really wants to ask. In general, QA dialogue system is the interaction between the User and the System. The aim to implement the QA system architecture is to * Author for correspondence

2 A Novel Model for QUestion Answering Railway System: QUARS provide the accurate answers to users and motivate Hindi Language and its users on the web. People speak various forms of languages as estimated near about Among top 100 languages, Hindi occupies the fifth position in the world 3. Thus, the system consists of many steps (Figure 1) such as: Query Processing, Query frame analyser, Dialogue history, Dialogue manager, SQL query generation and Answer generation. In this section, we give the brief introduction to QUARS and the motives behind designing of QA dialogue system. In the rest of this paper, we provide the pocket sketch of QA System (Section 1), Query processing technique (Section 2), Evaluation methodologies (Section 3) and Conclusion (Section 4). TREC is the chain of workshops that focuses on the various areas of Information Retrieval (IR) and provides the support and motivate the IR community for research. Each track provides challenges to participating groups with a set of problems. Problems might be documents, questions, or to access any target. Owing to TREC, we have best systems which enable us to answer more than two-thirds of factual questions. Researchers can present their own ideas, thoughts, research work etc. in the workshop. Many developers have proposed the system to help the User to find information but in a restricted manner. The best-known early program with restricted domain is BASEBALL 4, a program to provide answers about American league over one season. Other QA systems are START 2 and Wolfram Alfa 3. SHRDLU 5 is perhaps the first AI System to accept the user query and send a reply to the user. Another well remembered system in this tradition is the LUNAR system. This system provides the easy way to access, compare and assess the chemical analysis data of Lunar Rock 6. Initial dialogue systems such as UC (Unix Consultant) 7 and The Berkeley UNIX Consultant project (UC) provide the reply of user s query about UNIX operating system 8. Besides this, there has been a tremendous improvement in QA systems which support Indian languages such as Sravanthi et al. 9 designed a model with improvement of data management and query processing with Tourism as a domain. Currently, Vignesh et al. 10 introduced Hidden Markov Model (HMM) for speech recognition in Tamil language. In the same way, many researchers have contributed to the multilingual systems such as Hindi- Dogri 11, Punjabi 12 and Hindi-Kannada Architecture of QUARS The schematic diagram of QA dialogue system (Figure 1) focuses on Query processing (Section 2.1), Query frame analyzer (Section 2.2), Dialogue manager (Section 2.3), SQL generation (Section 2.4) and Answer generation (Section 2.5). Query processing is further segmented into three parts i.e. Tokenization (Section 2.1.1), Normalization (Section 2.1.2), and Keyword Selection (Section 2.1.3). Figure 1. Detailed architecture of QUARS. 2.1 Query Processing The processing of Natural language is a most complex task to be built. The system should be equipped with linguistic knowledge to analyse the query. Actually, it is a long pipeline of various modules to intercommunicate with each other to provide quick and brief answers in a short period of time. First and foremost module of the pipeline is input query step, e.g. when the Swarna Shatabdi goes to Delhi, and it consists of two modules i.e. Natural Language (NL) query and Transliteration (Figure 1). NL query receives the query from the user and Transliteration transliterate into Hindi language. The following is the example to input the query from user and transliterate into Hindi as follows: e.g:- Amritsar Express main mera kitna kiraya lagega? After Transliteration 2 Vol 9 (20) May Indian Journal of Science and Technology

3 Lovely Sharma, Vijay Dhir and Kamaljeet Kaur After this, linguistic processing undergoes three levels according to QA architecture such as: (i) Tokenization (Section 2.1.1) (ii) Normalization (Section 2.1.2) and (iii) Selecting keywords (Section 2.1.3) as shown in the following Figure 2. Figure 2. Query Processing Module Tokenization It is usual to concentrate on analysis of Natural Language Query while finding the primary units namely Tokens 14. However, it is impossible to proceed for further analysis without these tokens, clearly segregated. Thus, tokenization involves text that real needs to be segmented into linguistic units. The following is the illustration to tokenize the text. After Tokenization Normalization Many Non Standard representations of words may appear in the input query. The Non Standard Words (NSW) might be various dialects, also the impact of foreign languages and variation in alphabet leads to variation of the similar words. All these phonetic variations or Non Standard representations must typically be normalized. According to foregoing example, the variations may occur in कर य (Fare) word as shown below: कर य (kiraya), करय (kiaraya), कर य(Kiray), क र य (kiiraya), क र य (Kiiraya) Another illustration shows, widely used spelling variations for the station name jallandhar as: ज ल धर (Jaalandhar), (Jaalandhar), (jalandhar), जल धर (jalandhar) We recognize each one of the variations of a word in the language 15 and develop a solution to come up with a set of specific rules to language which can take care of such variations. It also influences the accuracy of the system i.e. only the correct word is selected from the knowledgebase, discarding all other variations Keyword Selection In the previous stage (Section 2.1.2), chunks are normalized and before the acceptance of these chunks, each chunk is compared with Knowledgebase through look-up table method until a word gets matched and that particular matched word is transformed into keyword. These keywords will help the system to find the purpose of the Natural language query.e.g. (Amritsar Express main mera kitna kiraya lagega? ) Keyword = कर य (Fare) In the above example कर य (Fare) is the keyword in query which exhibit the purpose of query i.e. user wants to enquire about the fare of the train. 2.2 Query Frame Analyser The precision of the system is greatly influenced by the selection of the Query Frame. At this stage, keywords are recognized and accepted by the system such as system identifies कर य (Fare) keyword as explained in the previous section (Section 2.1.3) and this keyword will be mapped against knowledge base again through look-up table method to find the appropriate Query Frame 16. Each natural language query has an individual Query frame. According to given example, कर य (Fare) related queryframe has following slots as shown in Table 1. Table 1. Query frame for fare Fare Assigned values Train Name/Code अम तसर Source ज ल धर Destination अम तसर Age 30 Class Vol 9 (20) May Indian Journal of Science and Technology 3

4 A Novel Model for QUestion Answering Railway System: QUARS Similarly, some more examples of stored query frames are arrival time, departure time, route related etc. Thus, the selection of Query Frame is hand-picked with the help of these keywords from the knowledgebase. 2.3 Dialogue Manager Sometimes, Slot-filling remains unfinished due to insufficient information given by the User. So, system will seek for more information. At this moment, DM enters into dialogue with the user and sends interactive messages to the user to know about the missing information as explained in Table 2. Thus, Dialogue Manager (DM) facilitates the interaction between the user and the system 17. The flow of the dialogue is controlled by the DM by deciding how the user and the system will respond to each other. 2.4 SQL Generation In this module, SQL Query generation is based on the completion of Query frame. In this situation, two cases arise: a). Query Frame is not complete b). Query Frame is complete. If Query Frame is not complete, DM will enter into dialogue with the user to clarify the information and if query frame is not complete, it generates the appropriate SQL query to provide the short and precise reply to the user. Table 3 depicts the conversion from Natural language query to SQL Query. 2.5 Answer Generation The user always wants succinct reply rather than web links, large documents etc. So, as discussed in the previous section, SQL Query is generated and is triggered on the Database to provide the accurate answer to the User. The response is redirected to the DM and passed on to the user interactively i.e. outcome from the SQL engine changes into a Natural language statement. According to the foregoing example, the reply for the query is 230 Rupees as shown below. select fare from TrainFare where (train_name= अम तसर,source_stat= ज ल धर,dest_stat= अम तसर, age=30,class= ) 230 Table 2. Example of dialogue with the user and filling of query frame User s Query Related Frame Assigned Value म : अम तसर म म र कतन कर य लग ग? :आपन कह स चलन ह? म : ज ल धर :आपन कह तक ज न ह? म : अम तसर म : 30 :आपक कतन आय ह? : आपन कस म सफ़र करन ह? म : क ड,?,?,?,?) क ड,चलन,?,?,?) क ड,चलन, पह चन,?,?) क ड,चलन, पह चन,आय,?) क ड,चलन, पह चन, आय,,?,?,?,?), ज ल धर,?,?,? ), ज ल धर, अम तसर,?,? ), ज ल धर, अम तसर, 30,? ), ज ल धर, अम तसर, 30, ) Table 3. Example of SQL Query generation Steps/Modules Example User s Query अम तसर म म र कतन कर य लग ग? Keyword कर य (Fare) Query Frame / क ड,चलन, पह चन, आय, (train name/code, source, destination, age, class) Assigned values, ज ल धर, अम तसर, 30, ) SQL Query select fare from TrainFare where (train_name= अम तसर,source_stat= ज ल धर,dest_stat= अम तसर, age=30,class= ) 4 Vol 9 (20) May Indian Journal of Science and Technology

5 Lovely Sharma, Vijay Dhir and Kamaljeet Kaur 3. Evaluation and Result The actual evaluation of success of any QA system is based on the satisfaction level of the User. As much as the User will satisfy, the success rate of QA system will raise too. But widely used parameters are Precision and Recall value 17. Precision = (Number of correct responses produced by the system / Number of responses produced by the system) * 100. Recall = (Number of correct responses produced by the system / Number of Natural queries given to the system) * 100. Number of Natural queries given to the system=150 Number of correct responses produced by the system=146 Number of responses produced by the system=149 Precision= (146/149)*100=98% Recall= (146/150)*100=97% By evaluating the QUARS, 150 natural language questions were fed into the QA system. As a result, the system generated Precision and Recall rates as 98% and 97% respectively. 4. Conclusion In this paper, we have implemented a keyword based system QUARS, employed for an Indian Railways system in Hindi language for Northern India. The system is extremely involved in Query Processing module. To implement this, the detailed analysis of input query has been done. For this, we do Tokenization (Section 2.1.1), Normalization (Section 2.1.2) and Keyword Selection (Section 2.1.3). The generated keywords (Section 2.1.3) help us to select appropriate Query frame and in the next step, SQL Query is generated with the help of these Query frames. At the end, system provides the answer to user in Hindi language. 5. References 1. Neves M, Leser U. Question answering for Biology. Methods Mar 1; 74(1): Barker K, Chaudhri VK, Chaw SY, Clark P, Fan J, Israel D, Mishra S, Porter BW, Romero P, Tecuci D, Yeh PZ. A question-answering system for AP. chemistry:assessing KR&R technologies. In Ninth International Conference on Principles of Knowledge Representation and Reasoning; p Available from: 3. Dave S, Bhattachary P, Klakowya D. Knowledge extraction from hindi text. IETE Technical Review. 2001; 18(4): Green BF, Wolf AK, Chomsky C, Laughery K. Baseball: an automatic question-answerer. Proceedings of IRE-AIEE- ACM 61 (Western) Papers presented at the western joint IRE-AIEE-ACM Computer Conference; 1963 May. p Ward N, SHRDLU. Encyclopaedia of Cognitive Science, Nature Publishing Group; Woods WA. Progress in natural language understanding -an application to lunar geology. Proceedings of the National Computer Conference and Exposition; p Chin DN. Knowledge structures in UC, the UNIX* Consultant. Proceedings of the Twenty-first American for Computational Linguistics; p Wilensky R, Chin DN, Luria M, Martin J, Mayfield J, Wu D. The Berkeley UNIX consultant project. Journal of Computational Linguistics. 1988; 14(4): Sravanthi MC, Prathyusha K,Mamidi R. A dialogue system for telugu, a resource-poor language. Computational Linguistics and Intelligent Text Processing, Springer International Publishing: Switerzerland; p Vignesh G, Ganesh SS. Tamil speech recognizer using hidden markov model for question answering system of railways. Advances in Intelligent Systems and Computing. Springer Verlag: Berl\in Heidelberg; p Dubey P. Testing and results of Hindi-dogri machine translation system. Indian Journal of Science and Technology Oct; 8(27):1 8. DOI: /ijst/2015/v8i27/ Puri R, Bedi RPS, Goyal V. Punjabi stemmer using punjabi wordnet database. Indian Journal of Science and Technology. 2015; 8(27):1 5. DOI: /ijst/2015/v8i27/ Kulkarni A, Srivatsa BR, Baji C. Hindi-Kannada named entity transliteration: issues and possible solutions. Indian Journal of Science and Technology Oct; 8(27):1 5. DOI: /ijst/2015/ v8i27/ Webster JJ, Kit C. Tokenization as the initial phase in NLP. Proceedings of the 14th Conference on Computational Linguistics, Nantes: France; p Sproat R, Black AW, Chen S, Kumar S, Ostendorf M, Richards C. Normalization of non-standard words. Computer Speech and Language. 2001; 15(3): Sinha RMK. On design of a question-answering interface for hindi in a restricted domain. International Conference on Artificial Intelligence, Las Vegas; p Reddy RRN, Bandyopadhyay S. Dialogue based question answering system in Telugu. Proceedings of the Workshop on Multilingual Question Answering; p Vol 9 (20) May Indian Journal of Science and Technology 5