Fast support vector machines for convolution tree kernels

Data Mining and Knowledge Discovery

Volumn 25, Issue 2, 2012, Pages 325-357

  Severyn, Aliaksei ^a   Moschitti, Alessandro ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Kernel methods; Large scale learning; Natural language processing; Tree kernels

Indexed keywords

CURRENT MODELS; CUTTING PLANE ALGORITHMS; DATA SETS; DIRECTED ACYCLIC GRAPHS; DIVERSE DOMAINS; FAST LEARNING; KERNEL METHODS; LARGE SCALE LEARNING; LEARNING FRAMEWORKS; NATURAL LANGUAGE PROCESSING; REAL-WORLD APPLICATION; SAMPLING STRATEGIES; STATE-OF-THE-ART METHODS; STRUCTURED DATA; TRAINING ALGORITHMS; TRAINING EXAMPLE; TREE KERNELS;

BIOINFORMATICS; COMPUTATIONAL LINGUISTICS; FORESTRY; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; SUPPORT VECTOR MACHINES;

TREES (MATHEMATICS);

ALGORITHMS; COMPUTATION; DATA PROCESSING; FORESTRY; SYSTEMS ENGINEERING;

EID: 84864558154     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0276-8     Document Type: Conference Paper

References (58)

1. Aiolli F, Da San Martino G, Sperduti A, Moschitti A (2006) Fast on-line kernel learning for trees. In: Proceedings of the 2006 IEEE conference on data mining. IEEE Computer Society, Los Alamitos, CA, pp 787-791. http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.69
2. Aiolli F, Martino GDS, Sperduti A,Moschitti A (2007) Efficient kernel-based learning for trees. In: CIDM, pp 308-315
3. Asai T, Abe K, Kawasoe S, Arimura H, Sakamoto H, Arikawa S (2002) Efficient substructure discovery from large semi-structured data. In: SDM
4. Bšdi R, Herr K, Joswig M (2011) Algorithms for symmetric linear and integer programs. Mathematical programming, series A, Online First. Comments: 21 pp, 1 figure; sums up and extends results from 0908.3329 and 0908.3331
5. Cancedda N, Gaussier E, Goutte C, Renders JM (2003) Word sequence kernels. J Mach Learn Res 3:1059-1082
6. Carreras X, Màrquez L (2005) Introduction to the CoNLL-2005 shared task: semantic role labeling. In: Proceedings of the 9th conference on natural language learning, CoNLL-2005, Ann Arbor
7. Charniak E (2000) A maximum-entropy-inspired parser. In: ANLP, pp 132-139
8. Chi Y, Yang Y, Muntz RR (2004) Hybridtreeminer: An efficient algorithm for mining frequent rooted trees and free trees using canonical form. In: SSDBM, pp 11-20
9. CollinsM,DuffyN(2002) Newranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In: ACL, pp 263-270
10. Daumé III H, Marcu D (2004) A tree-position kernel for document compression. In: Proceedings of the DUC, Boston
11. Denoyer L,Gallinari P (2007) Report on the xmlmining track at INEX 2005 and INEX2006: categorization and clustering of xml documents. SIGIR Forum 41:79-90
12. Franc V, Sonnenburg S (2008) Optimized cutting plane algorithm for support vector machines. In: ICML, pp 320-327
13. Giuglea AM, Moschitti A (2004) Knowledge Discovering using FrameNet, VerbNet and PropBank. In: Proceedings of the workshop on ontology and knowledge discovering at ECML 2004, Pisa, Italy
14. GiugleaAM,MoschittiA(2006) Semantic role labeling via framenet, verbnet and propbank. In: Proceedings of ACL, Sydney, Australia
15. Haussler D (1999) Convolution kernels on discrete structures. Tech. Rep. UCSC-CRL-99-10, University of California, Santa Cruz
16. Joachims T (1999) Making large-scale SVM learning practical. In: Advances in kernel methods-support vector learning, chap 11. MIT Press, Cambridge, pp 169-184
17. Joachims T (2005) A support vector method for multivariate performance measures. In: International conference on machine learning (ICML), pp 377-384
18. Joachims T (2006) Training linear SVMs in linear time. In: KDD
19. Joachims T, Yu CNJ (2009) Sparse kernel SVMS via cutting-plane training. Mach Learn 76(2-3):179-193
20. Kate RJ, Mooney RJ (2006) Using string-kernels for learning semantic parsers. In: ACL
21. Kuang R, Ie E, Wang K, Wang K, Siddiqi M, Freund Y, Leslie CS (2004) Profile-based string kernels for remote homology detection and motif extraction. In: 3rd international IEEE computer society computational systems bioinformatics conference (CSB 2004), pp 152-160
22. Kudo T, Matsumoto Y (2003) Fast methods for kernel-based text analysis. In: Proceedings of ACL'03
23. Leslie C, Eskin E, Cohen A,Weston J, NobleWS (2004) Mismatch string kernels for discriminative protein classification. Bioinformatics 20(4):467-476
24. Marcus M, Santorini B, Marcinkiewicz M (1993) Building a large annotated corpus of English: The Penn Treebank. Comput Linguist 19(2):313-330
25. Mehdad Y, Moschitti A, Zanzotto FM (2010) Syntactic/semantic structures for textual entailment recognition. In: HLT-NAACL, pp 1020-1028
26. Moschitti A (2004)A study on convolution kernel for shallow semantic parsing. In: Proceedings of ACL'04. Barcelona, Spain
27. Moschitti A (2006a) Efficient convolution kernels for dependency and constituent syntactic trees. In: Proceedings of ECML
28. MoschittiA(2006b) Making tree kernels practical for natural language learning. In: EACL. TheAssociation for Computer Linguistics
29. Moschitti A (2008) Kernel methods, syntax and semantics for relational text categorization. In: Proceeding of CIKM '08, New York, USA
30. Moschitti A, Bejan CA (2004) A semantic kernel for predicate argument classification. In: Ng HT, Riloff E (eds) HLT-NAACL 2004 workshop: eighth conference on computational natural language learning (CoNLL-2004). Association for Computational Linguistics, Boston, pp 17-24
31. Moschitti A, PighinD,BasiliR (2008) Tree kernels for semantic role labeling. Comput Linguist 34(2):193
32. Nguyen TVT,MoschittiA(2011) Joint distant and direct supervision for relation extraction. In: Proceedings of 5th international joint conference on natural language processing. Asian Federation of Natural Language Processing, Chiang Mai, Thailand, pp 732-740. http://www.aclweb.org/anthology/I11-1082
33. Noreen EW (1989) Computer-intensive methods for testing hypotheses : An introduction. Wiley-Interscience, New York
34. Padó S (2006) User's guide to sigf: significance testing by approximate randomisation
35. Palmer M, Kingsbury P, Gildea D (2005) The proposition bank: An annotated corpus of semantic roles. Comput Linguist 31(1):71-106
36. Pighin D, Moschitti A (2009a) Efficient linearization of tree kernel functions. In: Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009). Association for Computational Linguistics, Boulder, pp 30-38
37. Pighin D, Moschitti A (2009b) Reverse engineering of tree kernel feature spaces. In: Proceedings of the 2009 conference on empirical methods in natural language processing. Association for Computational Linguistics, Singapore, pp 111-120
38. Pighin D, Moschitti A (2010) On reverse feature engineering of syntactic tree kernels. In: Proceedings of the fourteenth conference on computational natural language learning. Association for Computational Linguistics, Uppsala, Sweden, pp 223-233
39. Rieck K, Krueger T, Brefeld U, Mueller KRs (2010) Approximate tree kernels. J Mach Learn Res 11:555-580
40. Saigo H, Vert J, Akutsu T, Ueda N (2004) Protein homology detection using string alignment kernels. Bioinformatics 20:1682-1689
41. SeverynA,MoschittiA(2010) Large-scale support vector learning with structural kernels. In:ECML/PKDD (3), pp 229-244
42. Severyn A, Moschitti A (2011) Fast support vector machines for structural kernels. In: ECML
43. Shasha D,Wang JTL, Zhang S (2004) Unordered tree mining with applications to phylogeny. In: ICDE, pp 708-719
44. Shervashidze N, Borgwardt K (2009) Fast subtree kernels on graphs. In: Proceedings of advances in neural information processing systems
45. Shi Q, Petterson J, Dror G, Langford J, Smola AJ, Vishwanathan SVN (2009) Hash kernels for structured data. JMLR 10:2615-2637
46. Steinwart I (2003) Sparseness of support vector machines. J Mach Learn Res 4:1071-1105
47. Termier A, Rousset MC, Sebag M (2004) Dryade: A new approach for discovering closed frequent trees in heterogeneous tree databases. In: ICDM, pp 543-546
48. Trentini F, Hagenbuchner M, Sperduti A, Scarselli F (2006) A self-organising map approach for clustering of xml documents. In: IJCNN, pp 1805-1812. IEEE
49. Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6:1453-1484
50. Veropoulos K, Campbell C, Cristianini N (1999) Controlling the sensitivity of support vector machines. In: Proceedings of the IJCAI, pp 55-60
51. Versley Y, Moschitti A, Poesio M, Yang X (2008) Coreference systems based on kernels methods. In: The 22nd International Conference on computational linguistics (Coling'08). Manchester, England
52. Wang C, Hong M, Pei J, Zhou H,WangW, Shi B (2004) Efficient pattern-growth methods for frequent tree pattern mining. In: PAKDD, pp 441-451
53. Wu G, Chang E (2003) Class-boundary alignment for imbalanced dataset learning. In: ICML2003workshop on learning from imbalanced data sets II, Washington, DC, pp 49-56
54. Xia Y, Yang Y (2005) Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Trans Knowl Data Eng 17(2):190-202
55. Yang LH, Lee ML, Hsu W, Guo X (2004) 2pxminer: An efficient two pass mining of frequent xml query patterns. In: KDD, pp 731-736
56. Yu CNJ, Joachims T (2008) Training structural svms with kernels using sampled cuts. In: KDD, pp 794-802
57. Zadrozny B, Langford J, Abe N (2003) Cost-sensitive learning by cost-proportionate example weighting. In: Proceedings of ICDM
58. Zaki MJ (2005) Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE TransKnowl Data Eng 17(8):1021-1035