Improving Accuracy of Answer Extraction in Question-Answer System – Research Paper Example

Download full paperFile format: .doc, available for editing

The paper "Improving Accuracy of Answer Extraction in Question-Answer System" is a great example of a research paper on logic and programming. Information technology has grown to the level that requires retrieving information from the Internet necessary. This is because the entire world is accessing information through the Internet, and the information is understood by human beings. Retrieving this information from a search engine such as yahoo and Google requires users who have adequate knowledge on a topic before searching. This limits individuals who have little knowledge of a certain title, thus requiring an accurate way of extracting an answer from such engines.

The current Question answering system (QA system) is a dedicated structure of information retrieval, and when offered with a set of documents, it tries to recover exact answers to questions fronted in the natural idiom. According to Zhenqiu (2012), the Open-domain question response needs question-answering systems to answer questions concerning every imaginable matter, and such systems cannot depend on the hand-designed domain for definite comprehension to stumble on and figure out the accurate answers. Besides that, several QA System has been employed, and diminutive effort has been made on the establishment of an assessment paradigm for them. QA system helps in retrieving information containing documents but does not provide means of locating the exact passages within the documents thus leaving the user with the task of extracting them.

This is a tedious exercise if a number of documents supplied are taken into account. Thus, it is essential to improve the way information is retrieved by reducing the number of documents and text that the user receives. Therefore having the most relevant extracted QA system needs to be improved.

There have been developments that are closer to providing accurate information retrieval systems like Corpus-based question answering, however, they have some shortcomings. In the system the find data or information in the form of natural language which is through the use of surface patterns or lexico-syntactic patterns to extract information from corpus offline.   Figure 1: General Architecture of Question Answering System Therefore the system to be developed here is intended to assist researchers to extract accurate information for their queries. This will combine the semantic-based search engine and accurate answer extraction system which will help in an accurate Question – answering system.

The system should have the ability to include the extraction mechanism and answer extraction since they are interrelated. The isolation of answer extraction and mechanism of extraction will not improve accuracy as expected because they are inseparable thus the need for a new system that will provide improvement is required. Research Field There are many fields of study relating to the QA system and they include query analysis, answer extraction, and information retrieval.

Query analysis involves the processing of information input in a natural language that is acceptable by the system to classify the information needed from the question format. Once the information has been queried, it will be retrieved from the system in a form of answers using information retrieval components into target collection. Yen, Wu, and Yang (2013) reviewed the existing QA system paradigms to review the status of comprehension with regard to comprehending user acceptance of novel information technologies. The procedure of searching answers to a natural language question has been presumed customarily to be in three different phases: first, question processing, which involves the acknowledgment of the information that is required from the question query (Hickl 1265).

Secondly, passage retrieval, which involves the retrieval of relevant information text from definite phrases and keywords mined from the passage of a question, and thirdly, extract parallel text to a question’ s precise answer. Huang and Yao (2004)argue that this is not the merely applicable model for factoid question answering (QA). As an alternative of extracting answers from groups of retrieved texts, the review paper brings in a new model that discerns precise answers to factoid questions by balancing a system of question-answer pairs, which correspond to all of the provided questions and answers that can be gained from a certain text collection.

The review paper analyzes available literature to provide an insight on the developed novel paradigm of answer extraction, which computes the value of candidate answers not just concerning a group of attributes mined from a question, but in the enormous perspective of the information entailed in the corpus all together.


Abne, Steven, Michael Collins and Amit Singhal. "Answer Extraction." Association for Neuro-Linguistic Programming (ANLP) (2000): 296-301.

Abouenour, Lahsen, Karim Bouzouba, and Paolo ,Rosso. ''An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering". International Journal on Information and Communication Technologies, Jun. 2010.13 May 2013.

Aktolga, Elif, James Allan and David A. Smith. "Passage Reranking for Question Answering Using Syntactic Structures and Answer Types." Advances in Information Retrieval 6611 (2011): 617-628.

Attardi, Giuseppe, et al. "PiQASso: Pisa Question Answering System." Text REtrieval Conference - TREC. Università di Pisa, Italy, 2001. 1-9.

Bilotti, MatthewW. and Eric Nyberg. "Improving Text Retrieval Precision and Answer Accuracy in Question Answering Systems." IRQA '08 Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering . Pittsburgh: Language Technologies Institute, 2008. 1-8 .

Buchholz, S and W Daelemans. "Complex Answers: A Case Study Using a WWW Question Answering System." Natural Language Engineering 7.4 (2001): 301-323.

Buscaldi, Davide and Paolo Rosso. Some Experiments in Question Answering with a Disambiguated Document Collection. Research Paper. Valencia, Spain: Natural Language Engineering Lab, 2007.

Buscaldi, Davide, et al. "Answering questions with an n-gram based passage retrieval engine." Journal of Intelligent Information Systems 34.2 (2010): 113-134.

Carpineto, Claudio And Giovanni Romano. "A Survey of Automatic Query Expansion in Information Retrieval." ACM Computing Surveys 44.1 (2011): 1-50.

Davide Buscaldi, Paolo Rosso. "Some Experiments in Question Answering with a Disambiguated Document Collection." Evaluating Systems for Multilingual and Multimodal Information Access 5706 (2009): 442-447.

Fan, Shixi, et al. "A new question analysis approach for community question answering system." International Journal of Asian Language Processing 19.3 (2009): 95-108.

García-cumbreras, M Á, F Martínez-santiago and L A Ureña-lópez. "Architecture and evaluation of BRUJA, a multilingual question answering system." Information Retrieval 15.5 (2012): 413-432.

Guda, Vanitha, Suresh Kumar Sanampudi And I.Lakshmi Manikyamba. "Approches For Question Answering Systems." International Journal of Engineering Science and Technology (IJEST) 3.2 (2011): 990-996.

Hickl, Andrew. "Answering Questions with Authority." Proceedings of the 17th ACM conference on Information and knowledge management . Texas: Language Computer Corporation, 2008. 1261-1269.

Huang, Gai-tai and Hsiu-hsen Yao. "Chinese question-answering system." Journal of Computer Science and Technology 19.4 (2004): 479-488.

Kang, Hai-Yan, Wen-Hua Liu and Qi-Yan Zhuang. "Study on intelligent question-answering system of restricted field." Journal Xihua University (Natural Sciences Edition) 27.2 (2008): 7-41, 97.

Kim, Min-kyoung and Han-joon Kim. "Design of Question Answering System with Automated Question Generation." Fourth International Conference on Networked Computing and Advanced Information Management. Seoul: Univ. of Seoul, 2008. 365-369.

Kolomiyets, Oleksandr and Marie-Francine Moens. "A survey on question answering technology from an information retrieval perspective." Information Sciences 181.24 (2011): 5412-5434.

Kosseim, Leila and Jamileh Yousefi. "Improving the performance of question answering with semantically equivalent answer patterns." Data & Knowledge Engineering 66.1 (2008): 53-67.

Liu, Duen-Ren, et al. "Integrating expert profile, reputation and link analysis for expert finding in question-answering websites." Information Processing & Management 49.1 (2013): 312-329.

Mengqiu, Wang. "A Survey of Answer Extraction Techniques in Factoid Question Answering." Association for Computational Linguistics (2006): 1-14.

Moreda, Paloma, et al. "Combining semantic information in question answering systems." Information Processing & Management 47.6 (2011): 870.

Mori, Tatsunori. "Japanese question-answering system using A* search and its improvement." ACM Transactions on Asian Language Information Processing 4.3 (2005): 280-304.

Oh, Hyo-Jung, Sung Hyon Myaeng and Myung-Gil Jang. "Semantic passage segmentation based on sentence topics for question answering." Information Sciences 117.18 (2007): 3696-3717.

Quan, Xiaojun, Liu Wenyin and B. Qiu. "Term Weighting Schemes for Question Categorization." IEEE Transactions on Pattern Analysis and Machine Intelligence 33.5 (2011): 1009 - 1021 .

Quarteroni, S and S Manandhar. "Designing an Interactive Open-Domain Question Answering System." Natural Language Engineering 15.1 (2009): 73-95.

Srihari, Rohini and Wei Li. "A Question Answering System Supported by Information Extraction." ANLC '00 Proceedings of the sixth conference on Applied natural language processing. Stroudsburg: Association for Computational Linguistics, 2000. 166-172 .

Tapeh, Ali Ghobadi and Maseud Rahgozar. "A knowledge-based question answering system for B2C eCommerce." Knowledge-Based Systems 21.8 (2008): 946-950.

Veeravalli, Surya Ganesh and Vasudeva Varma. "Passage Retrieval Using Answer Type Profiles in Question Answering." 23rd Pacific Asia Conference on Language, Information and Computation. Hyderabad, India: Language Technologies Research Centre, 2009. 559-569.

Vila, Katia, Jose-Norberto Maźon and Antonio Ferŕandez. "Model-driven adaptation of question answering systems for ambient intelligence by integrating restricted-domain knowledge." Procedia Computer Science 4 (2011): 1650-1659.

Vilares, Jesús, Manuel Vilares and Juan Otero. "Managing misspelled queries in IR applications." Information Processing & Management 47.2 (2011): 263-286.

Wacholder, Nina, et al. "A model for quantitative evaluation of an end-to-end question-answering system." Journal of the American Society for Information Science and Technology 58.8 (2007): 1082.

Wang, C., et al. "Relation extraction and scoring in DeepQA." IBM Journal of Research and Development 58.3.4 (2012): 9:1 - 9:12.

Yen, Show-Jane, et al. "A support vector machine-based context-ranking model for question answering." Information Sciences 224.1 (2013): 77-87.

Yu, Zheng-Tao, Xiao-Zhong Fan and Li-Rong Song. "Query expansion for specific question types in Chinese question answering system." Transactions of Beijing Institute of Technology 25.10 (2005): 880-884.

Zhenqiu, Liang. "Design of Automatic Question Answering System." Procedia Engineering 29 (2012): 981-985.

Download full paperFile format: .doc, available for editing
Contact Us