Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Large-scale information retrieval with latent semantic indexing
Information Sciences: an International Journal
An efficient algorithm for full text retrieval for multiple keywords
Information Sciences: an International Journal
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Trainable question-answering systems
Trainable question-answering systems
ExtrAns: Extracting Answers from Technical Texts
IEEE Intelligent Systems
Answer Filtering via Text Categorization in Question Answering Systems
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
SVM answer selection for open-domain question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An intelligent discussion-bot for answering student queries in threaded discussions
Proceedings of the 11th international conference on Intelligent user interfaces
Learning question classifiers: the role of semantic information
Natural Language Engineering
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
Enhanced answer type inference from questions using sequential models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
On the quality of resources on the Web: An information retrieval perspective
Information Sciences: an International Journal
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
BVideoQA: Online English-Chinese bilingual video question answering
Journal of the American Society for Information Science and Technology
Kernels on linguistic structures for answer extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Question classification using head words and their hypernyms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Exploiting Wikipedia and EuroWordNet to solve Cross-Lingual Question Answering
Information Sciences: an International Journal
Investigation of question classifier in question answering
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Advanced structural representations for question classification and answer re-ranking
ECIR'07 Proceedings of the 29th European conference on IR research
A semantic approach for question classification using WordNet and Wikipedia
Pattern Recognition Letters
Linguistic kernels for answer re-ranking in question answering systems
Information Processing and Management: an International Journal
Extracting named entities using support vector machines
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A Robust Passage Retrieval Algorithm for Video Question Answering
IEEE Transactions on Circuits and Systems for Video Technology
Information Sciences: an International Journal
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
Alternative second-order cone programming formulations for support vector classification
Information Sciences: an International Journal
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Modern information technologies and Internet services are suffering from the problem of selecting and managing a growing amount of textual information, to which access is often critical. Machine learning techniques have recently shown excellent performance and flexibility in many applications, such as artificial intelligence and pattern recognition. Question answering (QA) is a method of locating exact answer sentences from vast document collections. This paper presents a machine learning-based question-answering framework, which integrates a question classifier, simple document/passage retrievers, and the proposed context-ranking models. The question classifier is trained to categorize the answer type of the given question and instructs the context-ranking model to re-rank the passages retrieved from the initial retrievers. This method provides flexible features to learners, such as word forms, syntactic features, and semantic word features. The proposed context-ranking model, which is based on the sequential labeling of tasks, combines rich features to predict whether the input passage is relevant to the question type. We employ TREC-QA tracks and question classification benchmarks to evaluate the proposed method. The experimental results show that the question classifier achieves 85.60% accuracy without any additional semantic or syntactic taggers, and reached 88.60% after we employed the proposed term expansion techniques and a predefined related-word set. In the TREC-10 QA task, by using the gold TREC-provided relevant document set, the QA model achieves a 0.563 mean reciprocal rank (MRR) score, and a 0.342 MRR score is achieved after using the simple document and passage retrieval algorithms.