J. Ronald, T. Brachman, and . Anand, The process of knowledge discovery in databases, Advances in Knowledge Discovery and Data Mining, pp.37-57, 1996.

J. Hereth-correia, G. Stumme, R. Wille, and U. Wille, Conceptual knowledge discovery--a human-centered approach, Applied Artificial Intelligence, vol.17, issue.3, pp.281-302, 2003.
DOI : 10.1080/713827122

M. Usama, G. Fayyad, P. Piatetsky-shapiro, and . Smyth, From data mining to knowledge discovery: an overview, Advances in knowledge discovery and data mining, pp.1-34, 1996.

J. Poelmans, P. Elzinga, S. Viaene, and G. Dedene, Formal Concept Analysis in Knowledge Discovery: A Survey, Proceedings of the 18th international conference on Conceptual structures: from information to intelligence, ICCS'10, pp.139-153, 2010.
DOI : 10.1007/978-3-642-14197-3_15

R. Stanley and H. Astudillo, Ontology and semantic wiki for an Intangible Cultural Heritage inventory, 2013 XXXIX Latin American Computing Conference (CLEI), pp.1-12, 2013.
DOI : 10.1109/CLEI.2013.6670653

R. Stanley, H. Astudillo, V. Codocedo, and A. Napoli, A conceptual-kdd approach and its application to cultural heritage, Concept Lattices and their Applications, pp.163-174, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00880002

P. Valtchev, R. Missaoui, and R. Godin, Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges, Lecture Notes in Computer Science, vol.2961, pp.352-371, 2004.
DOI : 10.1007/978-3-540-24651-0_30

R. Wille, Why can concept lattices support knowledge discovery in databases?, Journal of Experimental & Theoretical Artificial Intelligence, vol.14, issue.2-3, pp.81-92, 2002.
DOI : 10.1007/s002870000127

R. 1. Vreeken, J. Tatti, and N. , Interesting Patterns, pp.105-134, 2014.
DOI : 10.1007/978-3-319-07821-2_5

S. O. Kuznetsov, On stability of a formal concept, Annals of Mathematics and Artificial Intelligence, vol.8, issue.3, pp.101-115, 2007.
DOI : 10.1007/s10472-007-9053-6

G. I. Webb and J. Vreeken, Efficient Discovery of the Most Interesting Associations, ACM Transactions on Knowledge Discovery from Data, vol.8, issue.3, p.15, 2014.
DOI : 10.1145/2601433

J. Cao, Z. Wu, and J. Wu, Scaling up cosine interesting pattern discovery: A depth-first method, Information Sciences, vol.266, issue.0, pp.31-46, 2014.
DOI : 10.1016/j.ins.2013.12.062

C. Hébert and B. Crémilleux, Mining Frequent ??-Free Patterns in Large Databases, Discov. Sci. Lecture Notes in Computer Science, vol.3735, pp.124-136, 2005.
DOI : 10.1007/11563983_12

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 1999.

B. Ganter and S. O. Kuznetsov, Pattern Structures and Their Projections, Concept. Struct. Broadening Base. Lecture Notes in Computer Science, vol.2120, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Scalable Estimates of Concept Stability, Form. Concept Anal, pp.161-176, 2014.
DOI : 10.1007/978-3-319-07248-7_12
URL : https://hal.archives-ouvertes.fr/hal-01095920

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Revisiting Pattern Structure Projections, Form. Concept Anal. Volume 9113 of LNAI 9113, pp.200-215, 2015.
DOI : 10.1007/978-3-319-19545-2_13
URL : https://hal.archives-ouvertes.fr/hal-01186719

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Efficient mining of association rules using closed itemset lattices, Information Systems, vol.24, issue.1, pp.25-46, 1999.
DOI : 10.1016/S0306-4379(99)00003-4

A. Frank and A. Asuncion, UCI Machine Learning Repository, 2010.

A. Buzmakov, E. Egho, N. Jay, S. O. Kuznetsov, A. Napoli et al., On mining complex sequential data by means of FCA and pattern structures, International Journal of General Systems, vol.18, issue.2
DOI : 10.1023/A:1007652502315
URL : https://hal.archives-ouvertes.fr/hal-01186715

J. Gen, G. Wong, A. Yang, and C. S. , A vector space model for automatic indexing, PRESS References 1. Salton, pp.613-620, 1975.

S. M. Wong, W. Ziarko, and P. C. Wong, Generalized vector spaces model in information retrieval, Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '85, pp.18-25, 1985.
DOI : 10.1145/253495.253506

G. Tsatsaronis and V. Panagiotopoulou, A generalized vector space model for text retrieval based on semantic relatedness, Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop on, EACL '09, pp.70-78, 2009.
DOI : 10.3115/1609179.1609188

J. Becker and D. Kuropka, Topic-based vector space model, Proceedings of the 6th International Conference on Business Information Systems, pp.7-12, 2003.

A. Polyvyanyy and D. Kuropka, A quantitative evaluation of the enhanced topicbased vector space model, 2007.

K. M. Hammouda and M. S. Kamel, Document Similarity Using a Phrase Indexing Graph Model, Knowledge and Information Systems, vol.18, issue.6, pp.710-727, 2004.
DOI : 10.1007/s10115-003-0118-5

O. Zamir and O. Etzioni, Web document clustering, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '98, pp.46-54, 1998.
DOI : 10.1145/290941.290956

A. Schenker, H. Bunke, M. Last, and A. Kandel, Clustering of web documents using graph representations Applied Graph Theory in Computer Vision and Pattern Recognition, pp.247-265, 2007.

B. Galitsky, Natural language question answering system: Technique of semantic headers, Advanced Knowledge International, 2003.

O. Zamir and O. Etzioni, Grouper: a dynamic clustering interface to Web search results, Computer Networks, vol.31, issue.11-16, pp.31-1361, 1999.
DOI : 10.1016/S1389-1286(99)00054-7

H. J. Zeng, Q. C. He, Z. Chen, W. Y. Ma, and J. Ma, Learning to cluster web search results, Proceedings of the 27th annual international conference on Research and development in information retrieval , SIGIR '04, pp.210-217, 2004.
DOI : 10.1145/1008992.1009030

B. Galitsky, D. Ilvovsky, S. Kuznetsov, and F. Strok, Finding Maximal Common Sub-parse Thickets for Multi-sentence Search, Lecture Notes in Computer Science, vol.8323, pp.39-57, 2014.
DOI : 10.1007/978-3-319-04534-4_4

R. Cole, P. Eklund, and G. Stumme, Document retrieval for e-mail search and discovery using formal concept analysis, Applied Artificial Intelligence, vol.17, issue.3, pp.257-280, 2003.
DOI : 10.1080/713827120

B. Koester, Conceptual Knowledge Retrieval with FooCA: Improving Web Search Engine Results with Contexts and Concept Hierarchies, Advances in Data Mining . Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, pp.176-190, 2006.
DOI : 10.1007/11790853_14

N. Messai, M. D. Devignes, A. Napoli, and M. Smail-tabbone, Many-valued concept lattices for conceptual clustering and information retrieval, In: ECAI, vol.178, pp.127-131, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00338671

C. Carpineto and G. Romano, A lattice conceptual clustering system and its application to browsing retrieval, Machine Learning, vol.14, issue.2, pp.95-122, 1996.
DOI : 10.1007/BF00058654

F. Strok, B. Galitsky, D. Ilvovsky, and S. Kuznetsov, Pattern Structure Projections for Learning Discourse Structures, Artificial Intelligence: Methodology, Systems, and Applications, pp.254-260, 2014.
DOI : 10.1007/978-3-319-10554-3_26

B. Ganter and S. O. Kuznetsov, Pattern Structures and Their Projections, Conceptual Structures: Broadening the Base, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

B. Galitsky, D. Ilvovsky, S. Kuznetsov, and F. Strok, Matching sets of parse trees for answering multi-sentence questions, Proc. Recent Advances in Natural Language Processing, 2013.

H. Lee, M. Recasens, A. Chang, M. Surdeanu, and D. Jurafsky, Joint entity and event coreference resolution across documents, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.489-500, 2012.

W. C. Mann and S. A. Thompson, Discourse description: Diverse linguistic analyses of a fund-raising text, 1992.
DOI : 10.1075/pbns.16

J. R. Searle, Speech acts : an essay in the philosophy of language, 1969.
DOI : 10.1017/CBO9781139173438

B. Galitsky and J. L. De-la-rosa, Concept-based learning of human behavior for customer relationship management, Information Sciences, vol.181, issue.10, pp.2016-2035, 2011.
DOI : 10.1016/j.ins.2010.08.027

P. Becker and J. H. Correia, The ToscanaJ Suite for Implementing Conceptual Information Systems, Ganter et al. [2], pp.324-348
DOI : 10.1007/11528784_17

B. Ganter, G. Stumme, and R. Wille, Formal Concept Analysis, Foundations and Applications, Lecture Notes in Computer Science, vol.3626, 2005.

B. Ganter and R. Wille, Formal concept analysis -mathematical foundations, 1999.

C. Glodeanu, Triadic factor analysis, Proceedings of the 7th International Conference on Concept Lattices and Their Applications CEUR Workshop Proceedings, pp.127-138, 2010.

R. Jäschke, A. Hotho, C. Schmitz, B. Ganter, and G. Stumme, TRIAS--An Algorithm for Mining Iceberg Tri-Lattices, Sixth International Conference on Data Mining (ICDM'06), pp.907-911, 2006.
DOI : 10.1109/ICDM.2006.162

R. Jäschke, A. Hotho, C. Schmitz, B. Ganter, and G. Stumme, Discovering shared conceptualizations in folksonomies, Web Semantics: Science, Services and Agents on the World Wide Web, vol.6, issue.1, pp.38-53, 2008.
DOI : 10.1016/j.websem.2007.11.004

F. Lehmann, R. Wille, G. Ellis, R. Levinson, and W. Rich, A triadic approach to formal concept analysis, Proceedings of the Third International Conference on Conceptual Structures, ICCS '95, pp.32-43, 1995.
DOI : 10.1007/3-540-60161-9_27

S. Rudolph, C. Troanc?-a, and D. , Membership constraints in formal concept analysis, Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015.

S. Rudolph, C. Troanc?-a, and D. , Towards a Navigation Paradigm for Triadic Concepts, Proceedings of the 13th International Conference on Formal Concept Analysis, pp.232-248, 2015.
DOI : 10.1007/978-3-319-19545-2_16

C. S?-ac?-area, N. Hernandez, R. Jäschke, and M. Croitoru, Investigating oncological databases using conceptual landscapes, Graph-Based Representation and Reasoning -21st International Conference on Conceptual Structures, pp.299-304, 2014.

R. Wille, The Basic Theorem of triadic concept analysis, Order, vol.23, issue.2, pp.149-158, 1995.
DOI : 10.1007/BF01108624

R. Wille, Begriffliche Wissensverarbeitung: Theorie und Praxis, Informatik-Spektrum, vol.23, issue.6, pp.357-369, 2000.
DOI : 10.1007/s002870000127

R. Wille, Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies, Ganter et al. [2], pp.1-33
DOI : 10.1007/11528784_1

R. Wille, Methods of Conceptual Knowledge Processing, 4th International Conference, pp.1-29, 2006.
DOI : 10.1007/11671404_1

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

S. V. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, Graph Kernels, J. Mach. Learn. Res, vol.11, pp.1201-1242, 2010.

H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda, gBoost: a mathematical programming approach to graph classification and regression, Machine Learning, pp.69-89, 2009.
DOI : 10.1007/s10994-008-5089-z

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, pp.487-499, 1994.

A. Veloso, W. M. Jr, and M. J. Zaki, Lazy Associative Classification, Sixth International Conference on Data Mining (ICDM'06), pp.645-654, 2006.
DOI : 10.1109/ICDM.2006.96

B. Ganter and S. Kuznetsov, Pattern Structures and Their Projections, Conceptual Structures: Broadening the Base, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

C. Helma and S. Kramer, A survey of the Predictive Toxicology Challenge 2000-2001, Bioinformatics, vol.19, issue.10, pp.1179-1182, 2000.
DOI : 10.1093/bioinformatics/btg084

N. Shervashidze, S. V. Vishwanathan, T. Petri, K. Mehlhorn, and K. M. Borgwardt, Efficient graphlet kernels for large graph comparison, Journal of Machine Learning Research -Proceedings Track, vol.5, pp.488-495, 2009.

H. Bunke and K. Shearer, A graph distance metric based on the maximal common subgraph, Pattern Recognition Letters, vol.19, issue.3-4, pp.255-259, 1998.
DOI : 10.1016/S0167-8655(97)00179-7

S. O. Kuznetsov, Scalable Knowledge Discovery in Complex Data with Pattern Structures, Lecture Notes in Computer Science, vol.8251, pp.30-39, 2013.
DOI : 10.1007/978-3-642-45062-4_3

N. Przulj, Biological network comparison using graphlet degree distribution, Bioinformatics, vol.23, 2003.

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 1997.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Barrère, R. Badonnel, and O. Festor, A SAT-based autonomous strategy for security vulnerability management, 2014 IEEE Network Operations and Management Symposium (NOMS), 2014.
DOI : 10.1109/NOMS.2014.6838309

M. Barrère, R. Badonnel, and O. Festor, Vulnerability Assessment in Autonomic Networks and Services: A Survey, IEEE Communications Surveys & Tutorials, vol.16, issue.2, pp.988-1004, 2014.
DOI : 10.1109/SURV.2013.082713.00154

M. Barrère, G. Betarte, and M. Rodríguez, Towards machine-assisted formal procedures for the collection of digital evidence, 2011 Ninth Annual International Conference on Privacy, Security and Trust, pp.32-35, 2011.
DOI : 10.1109/PST.2011.5971960

M. Barrère, Autonomic Knowledge Discovery for Security Vulnerability Prevention in Self-governing Systems, 2015.

R. Bendaoud, Y. Toussaint, and A. Napoli, PACTOLE: A Methodology and a System for Semi-automatically Enriching an Ontology from a Collection of Texts, Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning, pp.203-216, 2008.
DOI : 10.1007/978-3-540-70596-3_14
URL : https://hal.archives-ouvertes.fr/inria-00315530

V. Codocedo, I. Lykourentzou, and A. Napoli, A semantic approach to concept lattice-based information retrieval, Annals of Mathematics and Artificial Intelligence, vol.40, issue.1, pp.1-27, 2014.
DOI : 10.1007/s10472-014-9403-0
URL : https://hal.archives-ouvertes.fr/hal-01095859

S. Ferré and R. D. King, A dichotomic search algorithm for mining and learning in domain-specific logics, Fundam. Inform, vol.66, issue.12, pp.1-32, 2005.

S. Ferré and O. Ridoux, A Logical Generalization of Formal Concept Analysis, LNCS, vol.1867, pp.357-370, 2000.

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 1999.

C. López, V. Codocedo, H. Astudillo, and L. M. Cysneiros, Bridging the gap between software architecture rationale formalisms and actual architecture documents: An ontology-driven approach, Science of Computer Programming, vol.77, issue.1, pp.66-80, 2012.
DOI : 10.1016/j.scico.2010.06.009

C. D. Manning, P. Raghavan, and H. Schtze, Introduction to Information Retrieval, 2008.
DOI : 10.1017/CBO9780511809071

N. Messai, M. Devignes, A. Napoli, and M. Sma¨?lsma¨?l-tabbone, BR-Explorer: A sound and complete FCA-based retrieval algorithm (Poster), ICFCA, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00103947

X. Ou, S. Govindavajhala, and A. W. Appel, Mulval: A logic-based network security analyzer, Proceedings of the 14th Conference on USENIX Security Symposium SSYM'05, pp.8-8, 2005.

J. Poelmans, D. I. Ignatov, S. O. Kuznetsov, and G. Dedene, Formal concept analysis in knowledge processing: A survey on applications, Expert Systems with Applications, vol.40, issue.16, pp.6538-6560, 2013.
DOI : 10.1016/j.eswa.2013.05.009

S. Barnum, Standardizing Cyber Threat Intelligence Information with the Structured Threat Information eXpression (STIX) Technical report, The MITRE Corporation, 2013.

B. Sertkaya, A survey on how description logic ontologies benefit from formal concept analysis. CoRR, abs, 1107.

S. Staab and R. Studer, Handbook on Ontologies, 2009.

G. Stumme, Formal Concept Analysis, Handbook on Ontologies, pp.177-199, 2009.
DOI : 10.1007/978-3-540-92673-3_8
URL : https://hal.archives-ouvertes.fr/inria-00100665

B. E. Ulicny, J. J. Moskal, M. M. Kokar, K. Abe, and J. K. Smith, Inference and Ontologies, Cyber Defense and Situational Awareness, Advances in Information Security, 2014.
DOI : 10.1007/978-3-319-11391-3_9

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 1999.

J. Poelmans, S. O. Kuznetsov, D. I. Ignatov, and G. Dedene, Formal Concept Analysis in knowledge processing: A survey on models and techniques, Expert Systems with Applications, vol.40, issue.16, pp.6601-6623, 2013.
DOI : 10.1016/j.eswa.2013.05.007

R. Belohlávek, What is a Fuzzy Concept Lattice? II, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing -13th International Conference, pp.19-26, 2011.
DOI : 10.1007/978-3-642-21881-1_4

J. Poelmans, D. I. Ignatov, S. O. Kuznetsov, and G. Dedene, Fuzzy and rough formal concept analysis: a survey, International Journal of General Systems, vol.1, issue.3, pp.105-134, 2014.
DOI : 10.1007/978-3-642-34475-6_73

B. Ganter and S. Kuznetsov, Pattern Structures and Their Projections, Conceptual Structures: Broadening the Base, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

M. Kaytoue, S. O. Kuznetsov, A. Napoli, and S. Duplessis, Mining gene expression data with pattern structures in formal concept analysis, Information Sciences, vol.181, issue.10, pp.1989-2001, 2011.
DOI : 10.1016/j.ins.2010.07.007
URL : https://hal.archives-ouvertes.fr/hal-00541100

R. M. Bell and Y. Koren, Lessons from the Netflix prize challenge, ACM SIGKDD Explorations Newsletter, vol.9, issue.2, pp.75-79, 2007.
DOI : 10.1145/1345448.1345465

P. Du-boucher-ryan and D. Bridge, Collaborative Recommending using Formal Concept Analysis, Knowledge-Based Systems, vol.19, issue.5, pp.309-315, 2006.
DOI : 10.1016/j.knosys.2005.11.017

D. I. Ignatov, E. Nenova, N. Konstantinova, and A. V. Konstantinov, Boolean Matrix Factorisation for Collaborative Filtering: An FCA-Based Approach, Artificial Intelligence: Methodology, Systems, and Applications -16th International Conference, pp.47-58, 2014.
DOI : 10.1007/978-3-319-10554-3_5

F. Alqadah, C. Reddy, J. Hu, and H. Alqadah, Biclustering neighborhood-based collaborative filtering method for top-n recommender systems, Knowledge and Information Systems, vol.11, issue.8, pp.1-17, 2014.
DOI : 10.1007/s10115-014-0771-x

D. I. Ignatov, S. O. Kuznetsov, J. Poelmans, J. Vreeken, C. Ling et al., Concept-Based Biclustering for Internet Advertisement, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.123-130, 2012.
DOI : 10.1109/ICDMW.2012.100

D. Lemire and A. Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, Proceedings of the 2005 SIAM International Conference on Data Mining, pp.471-475
DOI : 10.1137/1.9781611972757.43

F. Cacheda, V. Carneiro, D. Fernández, and V. Formoso, Comparison of collaborative filtering algorithms, ACM Transactions on the Web, vol.5, issue.1, pp.1-233, 2011.
DOI : 10.1145/1921591.1921593

D. I. Ignatov, J. Poelmans, G. Dedene, and S. Viaene, A New Cross-Validation Technique to Evaluate Quality of Recommender Systems, Proceedings. Lecture Notes in Computer Science, vol.17, issue.3-4, pp.195-202, 2012.
DOI : 10.1007/s10791-007-9038-4

P. Cremonesi, Y. Koren, and R. Turrin, Performance of recommender algorithms on top-n recommendation tasks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.39-46, 2010.
DOI : 10.1145/1864708.1864721

F. Radlinski, K. Hofmann, P. Serdyukov, P. Braslavski, S. O. Kuznetsov et al., Practical Online Retrieval Evaluation, Advances in Information Retrieval -35th European Conference on IR Research , ECIR 2013'I':PRP, <16>NP'had':VBD, <17>NP'to':TO, <18>NP'use':VB, <19>NP'crampons':NNS], pp.878-881, 2013.
DOI : 10.1007/978-3-642-36973-5_107

. Np, N. Nn-ice, N. Dt-the, N. , N. Dt-the et al., DT-the IN-that NN-ice NN-axe MD-should VB-be VB-used ], VP [VB-* NN-* VB-use ], VP [DT-the IN-in ], VP [VB-reporting IN-in JJ-late NN-afternoon (TIME), DT-the JJ-late NN-afternoon VP [VB-crossing DT-the NN-snow NN-* IN-* ], VP [DT-the NN-* References 1. Borgida ER, DL McGuinness, Asking Queries about Frames. Proceedings of the 5th Int. Conf. on the Principles of Knowledge Representation and Reasoning, pp.340-349, 1996.

B. Maccartney, M. Galley, and C. D. Manning, A phrase-based alignment model for natural language inference, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, 2008.
DOI : 10.3115/1613715.1613817

B. Galitsky, Natural Language Question Answering System: Technique of Semantic Headers, Advanced Knowledge International, 2003.

B. Galitsky, J. Lluis-de-la-rosa, and G. Dobrocsi, Inferring the semantic properties of sentences by mining syntactic parse trees. Data & Knowledge Engineering, pp.21-45, 2012.

B. Galitsky, D. Usikov, and O. Sergei, Kuznetsov: Parse Thicket Representations for Answering Multi-sentence questions, 20th International Conference on Conceptual Structures, p.2013, 2013.

B. Galitsky, Machine Learning of Syntactic Parse Trees for Search and Classification of Text Engineering Application of AI, 2012.

B. Galitsky, Transfer learning of syntactic structures for building taxonomies for search engines, Engineering Applications of Artificial Intelligence, vol.26, issue.10, pp.2504-2515, 2013.
DOI : 10.1016/j.engappai.2013.08.010

B. Galitsky, D. Ilvovsky, S. Kuznetsov, and F. Strok, Matching sets of parse trees for answering multi-sentence questions. Recent Advances in Natural Language Processing, pp.13-1037

B. Galitsky, Learning parse structure of paragraphs and its applications in search, Engineering Applications of Artificial Intelligence, vol.32, issue.32, pp.160-184, 2014.
DOI : 10.1016/j.engappai.2014.02.013