978-1-4244-8551-2/10/$26.00 ©2010 IEEE ICIAfS10 Abstract— Using computers to answer natural language questions is an interesting and challenging problem. Generally such problems are handled under two categories: open domain problems and close domain problems. This paper presents a system that attempts to solve close domain problems. Typically, in a close domain, answers to questions are not available in the public domain and therefore they cannot be searched using a search engine. Hence answers have to be stored in a database by a domain expert. Then, the challenge is to understand the natural language question so that the solution could be matched to the respective answer in the database. We use a template matching technique to perform this matching. In addition, given that our target is to use this system with non-native English speakers, we developed a method to overcome the mismatches we might encounter due to spelling mistakes. The system is developed such that the questions can be asked using short messages from a mobile phone and therefore the system is designed to understand SMS language in addition to English. One of the main contributions of this paper is the outcome presented of a deployment of this system in a real environment. Keywords—FAQ, Answering System, SMS, Template Matching I. INTRODUCTION EVELOPING mechanisms for using computers to answer user questions is becoming an interesting problem with the increased use of computers. Such mechanisms allow users to ask questions in a natural language and give a concise and accurate answer. Understanding user questions in natural languages requires Natural Language Processing (NLP). Being an active area of research, NLP plays a big role in the ICT and Question Answering (QA) systems. Natural language processing is the computerized approach to analyzing text based on both a set of theories and a set of technologies. It will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword based retrieval mechanisms. The QA system can better satisfy the needs of users as they will provide an accurate, quicker, convenient and effective way of giving answers to user questions. The approach we have adopted in this project is an automated FAQ (Frequently Asked Question) answering system that replies with pre-stored answers to user questions asked in ordinary English, rather than keyword or syntax based retrieval mechanisms. This is achieved using a template matching technique with some other mechanisms like disemvoweling, matching synonyms, etc. The natural language processing technique developed for FAQ retrieval does not analyze user queries. Instead analysis is applied to FAQs in the database. Thus, the work of FAQ retrieval is reduced to keyword matching creating an illusion of intelligence. The system is both evolving and portable. Evolving because its question answering ability improves as more questions are asked and new FAQ entries are created. It is portable because the system could be used for any problem domain (closed) by changing the knowledge base. Typically, there are two types of question answering systems: (1) closed-domain question answering that deals with questions under a specific domain, and can be seen as an easier task on one hand as the NLP systems can exploit domain-specific knowledge frequently formalized in ontology but harder on the other as the information is not generally available in the public domain; and (2) open- domain question answering that deals with questions about nearly everything, and can rely only on general ontology and world knowledge. On the other hand, as mentioned earlier these systems usually have much more data available in the public domain from which to extract the answer. As depicted in Figure 1, there exist two methods [1], [2] for coming up with an appropriate answer for a user question and they are AI method and FAQ search method. The AI method [2] focuses on answer generation by analyzing questions and creating an “understanding” of the question. This requires complex and advanced linguistic analysis programs. There are three generic methods that an answer can be generated using stored FAQs and answers [3] and they are: (1) artificial intelligence approach; (2) statistical techniques; and (3) template matching. An Automatic Answering System with Template Matching for Natural Language Questions Tilani Gunawardena, Medhavi Lokuhetti, Nishara Pathirana, Roshan Ragel and Sampath Deegalla Faculty of Engineering, University of Peradeniya, Peradeniya 20400 Sri Lanka etilani@gmail.com, medhavimpl@gmail.com, nishara.pdn@gmail.com, roshanr@pdn.ac.lk and dsdeegalla@pdn.ac.lk D 353