Building a question answering test collection
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Question classification using support vector machines
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Experiments with open-domain textual Question Answering
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Question answering using maximum entropy components
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Statistical QA - classifier vs. re-ranker: what's the difference?
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Question answering as question-biased term extraction: a new approach toward multilingual QA
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System
IEICE - Transactions on Information and Systems
Interlingual Information Extraction as a Solution for Multilingual QA Systems
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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Our research is to investigate a machine learning approach in order to build an Indonesian Question Answering System. Based on our experiments result on the question classification task, we choose to use SVM as the machine learning algorithm. Similar with ordinary QA systems, we divide our system into three subcomponents: question classifier, passage retriever and answer finder. The SVM algorithm is employed in the question classifier and answer finder modules. To overcome the language resource poorness problem of Indonesian language, we introduce a bi-gram frequency attribute extracted from a downloaded newspaper corpus. The comparison among attribute combination is shown in our question classifier experiment. The t-test shows that the question shallow parser result attribute joined with bi-gram frequency attribute gives significant improvement compared to the baseline (bag of words). Our question classifier achieves 96% accuracy. We also compare some attribute combinations in the answer finder module. We find that the join attribute between the expected answer type (EAT) and the attributes of the question classifier gives higher MRR score than using only the EAT attribute or only the attribute of the question classifiers. Our QA system achieves MRR (Mean Reciprocal Rank) of 0.52 for exact answers.