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Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein.
Answering English questions by computer: A survey
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Interaction with texts: Information retrieval as information-seeking behavior
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Pivoted document length normalization
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SMART high precision: TREC 7
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Foundations of statistical natural language processing
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Falcon: Boosting knowledge for answer engines
Harabagiu, S., Moldovan, D., Pasca, M., Mihalcea, R., Surdeanu, M., Bunescu, R., Girju, R., Rus, V., & Morarescu, P. () · 2000
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Ontology learning for the semantic web
Maedche, A., & Staab, S. (2001) · 2001
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Overview of the TREC-9 question answering track
Voorhees, E. M. (2001) · 2001
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An analysis of the AskMSR question-answering system
Brill, E., Dumais, S., & Banko, M. (2002) · 2002
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The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · 2002
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Quasm: a system for question answering using semi-structured data
Pinto, D., Branstein, M., Coleman, R., Croft, W. B., King, M., Li, W., & Wei, X. (2002) · 2002
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Learning surface text patterns for a question answering system
Ravichandran, D., & Hovy, E. (2002) · 2002
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Performance issues and error analysis in an open-domain question answering system
Moldovan, D., Paşca, M., Harabagiu, S., & Surdeanu, M. (2003) · 2003
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Question answering on lecture videos: A multifaceted approach
Cao, J., & Nunamaker, J. F. (2004) · 2004
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Question answering by searching large corpora with linguistic methods
Kaisser, M., & Becker, T. (2004) · 2004
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Open-domain question answering from large text collections
Pasca, M. (2005) · 2005
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Probabilistic question answering on the web
Radev, D., Fan, W., Qi, H., Wu, H., & Grewal, A. (2005) · 2005
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An ontology-based information retrieval model
Vallet, D., Fernández, M., & Castells, P. (2005) · 2005
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Exploring correlation of dependency relation paths for answer extraction
Shen, D., & Klakow, D. (2006) · 2006
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An exploration of the principles underlying redundancy-based factoid question answering
Lin, J. (2007) · 2007
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Aqualog: An ontology-driven question answering system for organizational semantic intranets
Lopez, V., Uren, V., Motta, E., & Pasin, M. (2007) · 2007
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Question answering in restricted domains: An overview
Mollá, D., & Vicedo, J. L. (2007) · 2007
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Applying question answering technology to locating malevolent online content
Roussinov, D., & Robles-Flores, J. A. (2007) · 2007
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Leveraging question answer technology to address terrorism inquiry
Schumaker, R. P., & Chen, H. (2007) · 2007
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Finding the right facts in the crowd
Bian, J., Liu, Y., Agichtein, E., & Zha, H. (2008) · 2008
MCTest: A challenge dataset for the open-domain machine comprehension of text
Richardson, M. (2013) · 2013
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Weighted extreme learning machine for imbalance learning
Zong, W., Huang, G.-B., & Chen, Y. (2013) · 2013
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Glove: Global vectors for word representation
Pennington, J., Socher, R., & Manning, C. (2014) · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., & Bengio, Y. (2015) · 2015
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User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit
Giboney, J. S., Brown, S. A., Lowry, P. B., & Nunamaker, J. F. (2015) · 2015
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Deep learning in neural networks: An overview
Schmidhuber, J. (2015) · 2015
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SpidersRUs: Creating specialized search engines in multiple languages
Chau, M., Qin, J., Zhou, Y., Tseng, C., & Chen, H. (2008) · 2008
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How to select an answer string?
Echihabi, A., Hermjakob, U., Hovy, E., Marcu, D., Melz, E., & Ravichandran, D. (2008) · 2008
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Addressing ontology-based question answering with collections of user queries
Ferrández, Ó., Izquierdo, R., Ferrández, S., & Vicedo, J. L. (2009) · 2009
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Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition
Jurafsky, D. S., & Martin, J. H. (2009) · 2009
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Exploratory undersampling for class-imbalance learning
Liu, X.-Y., Wu, J., & Zhou, Z.-H. (2009) · 2009
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The probabilistic relevance framework: Bm25 and beyond
Robertson, S. (2009) · 2009
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Deep learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016) · 2016
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Key-value memory networks for directly reading documents
Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A., & Weston, J. (2016) · 2016
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How transferable are neural networks in nlp applications?
Mou, L., Meng, Z., Yan, R., Li, G., Xu, Y., Zhang, L., & Jin, Z. (2016) · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · 2016
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Question answering on freebase via relation extraction and textual evidence
Xu, K., Reddy, S., Feng, Y., Huang, S., & Zhao, D. (2016) · 2016
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Reading wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · 2017
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Decision support from financial disclosures with deep neural networks and transfer learning
Kraus, M., & Feuerriegel, S. (2017) · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · 2017
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Bidirectional attention flow for machine comprehension
Seo, M., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2017) · 2017
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Adaptive document retrieval for deep question answering
Kratzwald, B., & Feuerriegel, S. (2018) · 2018
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Deep learning for affective computing: Text-based emotion recognition in decision support
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018) · 2018
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Deep learning in business analytics and operations research: Models, applications and managerial implications
Kraus, M., Feuerriegel, S., & Oztekin, A. (2018) · 2018
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Deep bayesian active learning for natural language processing: Results of a large-scale empirical study
Siddhant, A., & Lipton, Z. C. (2018) · 2018
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R3: Reinforced ranker-reader for open-domain question answering
Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · 2018
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