Elamur Research
☰
home / papers / 2014 / Frame-Semantic Parsing

Frame-Semantic Parsing

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/J14-1002.pdf

Summary

The paper addresses frame-semantic parsing. Given an English sentence, the system finds words and phrases that evoke frames from the FrameNet lexicon, assigns each one a frame, and labels the spans that fill that frame's roles. The authors split the task into three steps: target identification, frame identification and argument identification.

  • Target identification uses heuristic rules adapted from Johansson and Nugues (2007).
  • Frame identification uses a discriminative model with latent variables. A graph-based semi-supervised method with sparsity-inducing penalties extends it to predicates that FrameNet does not cover.
  • Argument identification uses a feature-based model. All arguments of a frame are decoded jointly with AD3, an exact dual decomposition algorithm that enforces linguistic constraints such as no overlapping arguments.

The system, released as the open-source parser SEMAFOR, beats the previous best results on the SemEval 2007 benchmark at every stage and end to end. It also reports stronger results on the larger FrameNet 1.5 release. This matters because it makes frame semantics practical as a computational representation for downstream NLP, even with little annotated data.

Key points

  • The parser, SEMAFOR (Semantic Analyzer of Frame Representations), is open source. Its evaluation covers both the SemEval 2007 shared task on extracting frame-semantic structures and FrameNet 1.5.
  • Graph-based semi-supervised learning expands the small FrameNet predicate lexicon, so the parser can assign frames to predicates unseen in training.
  • AD3 gives better structures than local decoding that ignores constraints. It runs much faster than a commercial ILP solver and only about twice as slowly as inexact beam search.
  • Scores are clearly higher on FrameNet 1.5 than on SemEval 2007, which the authors attribute to more full-text annotations and a larger lexicon.
  • The authors compare the training data with PropBank, which has about 40,000 annotated sentences. They see more annotation and semi-supervised argument identification as the main ways to improve further.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Semantic role labeling

Cited by

119 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 114 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00163

@misc{das2014frame,
  title = {{Frame-Semantic Parsing}},
  author = {Das, Dipanjan and Chen, Desai and Martins, André F. T. and Schneider, Nathan and Smith, Noah A.},
  year = {2014},
  doi = {10.1162/coli\_a\_00163},
  url = {https://doi.org/10.1162/coli_a_00163}
}

References

104 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Auli, Michael and Adam Lopez. 2011. A comparison of loopy belief propagation and dual decomposition for integrated CCG supertagging and parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 470–480, Portland, OR. find
  2. Baker, Collin, Michael Ellsworth, and Katrin Erk. 2007. SemEval-2007 task 19: Frame semantic structure extraction. In Proceedings of the Fourth International Workshop on Semantic Evaluations, pages 99–104, Prague. find
  3. Baluja, Shumeet, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, and Mohamed Aly. 2008. Video suggestion and discovery for YouTube: taking random walks through the view graph. In Proceedings of the 17th International Conference on the World Wide Web, pages 895–9… find
  4. Bauer, Daniel and Owen Rambow. 2011. Increasing coverage of syntactic subcategorization patterns in FrameNet using VerbNet. In Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing, pages 181–184, Washington, DC. find
  5. Bejan, Cosmin A. 2009. Learning Event Structures From Text. Ph.D. thesis, The University of Texas at Dallas. find
  6. Bengio, Yoshua, Olivier Delalleau, and Nicolas Le Roux. 2006. Label propagation and quadratic criterion. In Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, editors, Semi-Supervised Learning. MIT Press, Cambridge, MA, pages 193–216. find
  7. Boas, Hans C. 2002. Bilingual FrameNet dictionaries for machine translation. In Proceedings of the Third International Conference on Language Resources and Evaluation, pages 1,364–1,371, Las Palmas. find
  8. Bottou, Léon. 2004. Stochastic learning. In Olivier Bousquet and Ulrike von Luxburg, editors, Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence, LNAI 3176. Springer Verlag, Berlin, pages 146–168. find
  9. Burbea, Jacob and Calyampudi R. Rao. 2006. On the convexity of some divergence measures based on entropy functions. IEEE Transactions on Information Theory, 28(3):489–495. find
  10. Burchardt, Aljoscha, Katrin Erk, and Anette Frank. 2005. A WordNet detour to FrameNet. In Bernhard Fisseni, Hans-Christian Schmitz, Bernhard Schröder, and Petra Wagner, editors, Sprachtechnologie, mobile Kommunikation und linguistische Resourcen, volume 8 of Computer Studies in Language and Speech.… find
  11. Burchardt, Aljoscha and Anette Frank. 2006. Approaching textual entailment with LFG and FrameNet frames. In Proceedings of the Second PASCAL RTE Challenge Workshop, pages 92–97, Venice. find
  12. Burchardt, Aljoscha, Marco Pennacchiotti, Stefan Thater, and Manfred Pinkal. 2009. Assessing the impact of frame semantics on textual entailment. Natural Language Engineering, 15(4):527–550. find
  13. Carreras, Xavier and Lluı́s Màrquez. 2004. Introduction to the CoNLL-2004 shared task: Semantic role labeling. In Proceedings of the Eighth Conference on Computational Natural Language Learning, pages 89–97, Boston, MA. find
  14. Carreras, Xavier and Lluı́s Màrquez. 2005. Introduction to the CoNLL-2005 shared task: Semantic role labeling. In Proceedings of the Ninth Conference on Computational Natural Language Learning, pages 152–164, Ann Arbor, MI. find our card
  15. Chang, Yin-Wen and Michael Collins. 2011. Exact decoding of phrase-based translation models through Lagrangian relaxation. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 26–37, Edinburgh. find
  16. Chapelle, Olivier, Bernhard Schölkopf, and Alexander Zien, editors. 2006. Semi-Supervised Learning. MIT Press, Cambridge, MA. find
  17. Chen, Desai, Nathan Schneider, Dipanjan Das, and Noah A. Smith. 2010. SEMAFOR: Frame argument resolution with log-linear models. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 264–267, Upssala. find
  18. Corduneanu, Adrian and Tommi Jaakkola. 2003. On information regularization. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, pages 151–158, Acapulco. find
  19. Das, Dipanjan, André F. T. Martins, and Noah A. Smith. 2012. An exact dual decomposition algorithm for shallow semantic parsing with constraints. In Proceedings of the First Joint Conference on Lexical and Computational Semantics, pages 209–217, Montréal. find
  20. Das, Dipanjan and Slav Petrov. 2011. Unsupervised part-of-speech tagging with bilingual graph-based projections. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 600–609, Portland, OR. find
  21. Das, Dipanjan, Nathan Schneider, Desai Chen, and Noah A. Smith. 2010. Probabilistic frame-semantic parsing. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 948–956, Los Angeles, CA. find
  22. Das, Dipanjan and Noah A. Smith. 2011. Semi-supervised frame-semantic parsing for unknown predicates. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1,435–1,444, Portland, OR. find
  23. Das, Dipanjan and Noah A. Smith. 2012. Graph-based lexicon expansion with sparsity-inducing penalties. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 677–687, Montréal. find
  24. Dean, Jeffrey and Sanjay Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107–113. find
  25. DeNero, John and Klaus Macherey. 2011. Model-based aligner combination using dual decomposition. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 420–429, Portland, OR. find
  26. Deschacht, Koen and Marie-Francine Moens. 2009. Semi-supervised semantic role labeling using the Latent Words Language Model. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 21–29, Singapore. find
  27. Dhillon, Paramveer S., Partha Pratim Talukdar, and Koby Crammer. 2010. Learning better data representation using inference-driven metric learning. In Proceedings of the ACL 2010 Conference Short Papers, pages 377–381, Uppsala. find
  28. Erk, Katrin and Sebastian Padó. 2006. Shalmaneser—a toolchain for shallow semantic parsing. In Proceedings of the Fifth International Conference on Language Resources and Evaluation, pages 527–532, Genoa. find
  29. Fellbaum, Christiane, editor. 1998. WordNet: an electronic lexical database. MIT Press, Cambridge, MA. find
  30. Fillmore, Charles J. 1982. Frame semantics. In Linguistics in the Morning Calm. Hanshin Publishing Co., Seoul, pages 111–137. find
  31. Fillmore, Charles J., Christopher R. Johnson, and Miriam R. L. Petruck. 2003. Background to FrameNet. International Journal of Lexicography, 16.3:235–250. find
  32. Fleischman, Michael, Namhee Kwon, and Eduard Hovy. 2003. Maximum entropy models for FrameNet classification. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 49–56, Sapporo. find
  33. Fung, Pascale and Benfeng Chen. 2004. BiFrameNet: Bilingual frame semantics resource construction by cross-lingual induction. In Proceedings of the 20th International Conference on Computational Linguistics, pages 931–937, Geneva. find
  34. Fürstenau, Hagen and Mirella Lapata. 2009a. Graph alignment for semi-supervised semantic role labeling. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 11–20, Singapore. find
  35. Fürstenau, Hagen and Mirella Lapata. 2009b. Semi-supervised semantic role labeling. In Proceedings of the 12th Conference of the European Chapter of the ACL, pages 220–228, Athens. find
  36. Fürstenau, Hagen and Mirella Lapata. 2012. Semi-supervised semantic role labeling via structural alignment. Computational Linguistics, 38(1):135–171. find
  37. Gerber, Matthew and Joyce Chai. 2010. Beyond NomBank: A study of implicit arguments for nominal predicates. In Proceedings of ACL, pages 1,583–1,592, Uppsala. find
  38. Gildea, Daniel and Daniel Jurafsky. 2002. Automatic labeling of semantic roles. Computational Linguistics, 28(3):245–288. find our card
  39. Girju, Roxana, Preslav Nakov, Vivi Nastase, Stan Szpakowicz, Peter Turney, and Deniz Yuret. 2007. SemEval-2007 task 04: Classification of semantic relations between nominals. In Proceedings of the Fourth International Workshop on Semantic Evaluations, pages 13–18, Prague. find
  40. Giuglea, Ana-Maria and Alessandro Moschitti. 2006. Shallow semantic parsing based on FrameNet, VerbNet and PropBank. In Proceedings of the 17th European Conference on Artificial Intelligence, pages 563–567, Amsterdam. find
  41. Gropp, W., E. Lusk, and A. Skjellum. 1994. Using MPI: Portable Parallel Programming with the Message-Passing Interface. MIT Press, Cambridge, MA. find
  42. Hajič, Jan, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martı́, Lluı́s Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Štěpánek, Pavel Straňák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. 2009. The CoNLL-2009 shared task: Syntactic and semantic dependen… find our card
  43. Ide, Nancy and Jean Véronis. 1998. Introduction to the special issue on word sense disambiguation: The state of the art. Computational Linguistics, 24(1):2–40. find
  44. Johansson, Richard and Pierre Nugues. 2007. LTH: Semantic structure extraction using nonprojective dependency trees. In Proceedings of the 4th International Workshop on Semantic Evaluations, pages 227–230, Prague. find
  45. Johansson, Richard and Pierre Nugues. 2008. Dependency-based semantic role labeling of PropBank. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 69–78, Honolulu, HI. find our card
  46. Kingsbury, Paul and Martha Palmer. 2002. From TreeBank to PropBank. In Proceedings of the 3rd International Conference on Language Resources and Evaluation, pages 1,989–1,993, Las Palmas. find our card
  47. Komodakis, Nikos, Nikos Paragios, and Georgios Tziritas. 2007. MRF optimization via dual decomposition: Message-passing revisited. In Eleventh International Conference on Computer Vision, pages 1–8, Rio de Janeiro. find
  48. Koo, Terry, Alexander M. Rush, Michael Collins, Tommi Jaakkola, and David Sontag. 2010. Dual decomposition for parsing with non-projective head automata. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1,288–1,298, Cambridge, MA. find
  49. Kowalski, Matthieu and Bruno Torrésani. 2009. Sparsity and persistence: Mixed norms provide simple signal models with dependent coefficients. Signal, Image and Video Processing, 3:251–264. find
  50. Lang, Joel and Mirella Lapata. 2010. Unsupervised induction of semantic roles. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 939–947, Los Angeles, CA. find
  51. Lang, Joel and Mirella Lapata. 2011. Unsupervised semantic role induction with graph partitioning. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1320–1331, Edinburgh. find
  52. Lin, Dekang. 1993. Principle-based parsing without overgeneration. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pages 112–120, Columbus, OH. find
  53. Lin, Dekang. 1994. Principar–an efficient, broad-coverage, principle-based parser. In Proceedings of the 15th Conference on Computational Linguistics, pages 482–488, Kyoto. find
  54. Lin, Dekang. 1998. Automatic retrieval and clustering of similar words. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pages 768–774, Montreal. find
  55. Lin, Jianhua. 1991. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1):145–151. find
  56. Litkowski, Kenneth C. and Orin Hargraves. 2007. SemEval-2007 task 06: Word-sense disambiguation of prepositions. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), pages 24–29, Prague. find
  57. Liu, Dong C. and Jorge Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(3):503–528. find
  58. Marcus, Mitchell P., Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: the Penn treebank. Computational Linguistics, 19(2):313–330, June. find our card
  59. Màrquez, Lluı́s, Xavier Carreras, Kenneth C. Litkowski, and Suzanne Stevenson. 2008. Semantic role labeling: an introduction to the special issue. Computational Linguistics, 34(2):145–159, June. find
  60. Martins, André F. T., Mario A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, and Eric P. Xing. 2011a. An augmented Lagrangian approach to constrained MAP inference. In Proceedings of the 28th International Conference on Machine Learning, pages 169–176, Bellevue, WA. find
  61. Martins, André F. T., Noah A. Smith, Pedro M. Q. Aguiar, and Mario A. T. Figueiredo. 2011b. Dual decomposition with many overlapping components. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 238–249, Edinburgh. find
  62. Martins, André F. T., Noah A. Smith, and Eric P. Xing. 2009. Concise integer linear programming formulations for dependency parsing. In Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natur… find
  63. Martins, André F. T., Noah A. Smith, Eric P. Xing, Mario A. T. Figueiredo, and Pedro M. Q. Aguiar. 2010. Turbo parsers: Dependency parsing by approximate variational inference. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 34–44, Cambridge, MA. find
  64. Matsubayashi, Yuichiroh, Naoaki Okazaki, and Jun’ichi Tsujii. 2009. A comparative study on generalization of semantic roles in FrameNet. In Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Na… find
  65. McDonald, Ryan, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 91–98, Ann Arbor, MI. find our card
  66. Meyers, Adam, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young, and Ralph Grishman. 2004. The NomBank project: An interim report. In Proceedings of the NAACL-HLT Workshop on Frontiers in Corpus Annotation, pages 24–31, Boston, MA. find
  67. Moschitti, Alessandro, Paul Morarescu, and Sanda M. Harabagiu. 2003. Open-domain information extraction via automatic semantic labeling. In Ingrid Russell and Susan M. Haller, editors, Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, pages 397–4… find
  68. Narayanan, Srini and Sanda Harabagiu. 2004. Question answering based on semantic structures. In Proceedings of the 20th International Conference on Computational Linguistics, Geneva. find
  69. Padó, Sebastian and Katrin Erk. 2005. To cause or not to cause: cross-lingual semantic matching for paraphrase modelling. In Proceedings of the Cross-Language Knowledge Induction Workshop, Cluj-Napoca. find
  70. Pado, Sebastian and Mirella Lapata. 2005. Cross-linguistic projection of role-semantic information. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pages 859–866, Vancouver. find
  71. Pennacchiotti, Marco, Diego De Cao, Roberto Basili, Danilo Croce, and Michael Roth. 2008. Automatic induction of FrameNet lexical units. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 457–465, Honolulu, HI. find
  72. Pradhan, Sameer S., Wayne H. Ward, Kadri Hacioglu, James H. Martin, and Dan Jurafsky. 2004. Shallow semantic parsing using support vector machines. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 233–2… find
  73. Punyakanok, Vasin, Dan Roth, and Wen-tau Yih. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics, 34(2):257–287. find our card
  74. Punyakanok, Vasin, Dan Roth, Wen-tau Yih, and Dav Zimak. 2004. Semantic role labeling via integer linear programming inference. In Proceedings of the 20th International Conference on Computational Linguistics, pages 1,346–1,352, Geneva. find
  75. Ratnaparkhi, Adwait. 1996. A maximum entropy model for part-of-speech tagging. In Proceedings of the 1996 Empirical Methods in Natural Language Processing, pages 133–142, Copenhagen. find
  76. Riedel, Sebastian and James Clarke. 2006. Incremental integer linear programming for non-projective dependency parsing. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 129–137, Sydney. find
  77. Roth, Dan and Wen-tau Yih. 2004. A linear programming formulation for global inference in natural language tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning, pages 1–8, Boston, MA. find our card
  78. Ruppenhofer, Josef, Michael Ellsworth, Miriam R. L. Petruck, Christopher R. find
  79. Johnson, and Jan Scheffczyk. 2006. FrameNet II: extended theory and practice. International Computer Science Institute, Berkeley, CA. find
  80. Rush, Alexander M. and Michael Collins. 2011. Exact decoding of syntactic translation models through Lagrangian relaxation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 72–82, Portland, OR. find
  81. Rush, Alexander M, David Sontag, Michael Collins, and Tommi Jaakkola. 2010. On dual decomposition and linear programming relaxations for natural language processing. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1–11, Cambridge, MA. find
  82. Schuler, Karin K. 2005. VerbNet: a broad-coverage, comprehensive verb lexicon. Ph.D. thesis, University of Pennsylvania. find
  83. Sha, Fei and Fernando Pereira. 2003. Shallow parsing with conditional random fields. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 134–141, Edmonton. find
  84. Shen, Dan and Mirella Lapata. 2007. Using semantic roles to improve question answering. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 12–21, Prague. find
  85. Shi, Lei and Rada Mihalcea. 2004. An algorithm for open text semantic parsing. In Proceedings of Workshop on Robust Methods in Analysis of Natural Language Data, pages 59–67, Geneva. find
  86. Shi, Lei and Rada Mihalcea. 2005. Putting pieces together: Combining FrameNet, VerbNet and WordNet for robust semantic parsing. In Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, pages 100–111, Mexico City. find
  87. Smith, David A. and Jason Eisner. 2008. Dependency parsing by belief propagation. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 145–156, Honolulu, HI. find
  88. Subramanya, Amarnag and Jeff Bilmes. 2008. Soft-supervised learning for text classification. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 1,090–1,099, Honolulu, HI. find
  89. Subramanya, Amarnag, Slav Petrov, and Fernando Pereira. 2010. Efficient graph-based semi-supervised learning of structured tagging models. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 167–176, Cambridge, MA. find
  90. Surdeanu, Mihai, Sanda Harabagiu, John Williams, and Paul Aarseth. 2003. Using predicate-argument structures for information extraction. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, pages 8–15, Sapporo. find
  91. Surdeanu, Mihai, Richard Johansson, Adam Meyers, Lluı́s Màrquez, and Joakim Nivre. 2008. find
  92. The CoNLL 2008 shared task on joint parsing of syntactic and semantic dependencies. In Proceedings of the Twelfth Conference on Computational Natural Language Learning, pages 159–177, Manchester. find our card
  93. Szummer, Martin and Tommi Jaakkola. 2001. Partially labeled classification with Markov random walks. In Advances in Neural Information Processing Systems 14, pages 945–952, Vancouver. find
  94. Talukdar, Partha Pratim and Koby Crammer. 2009. New regularized algorithms for transductive learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pages 442–457, Bled. find
  95. Thompson, Cynthia A., Roger Levy, and Christopher D. Manning. 2003. A generative model for semantic role labeling. In Proceedings of the European Conference on Machine Learning, pages 397–408, Cavtat-Dubrovnik. find
  96. Titov, Ivan and Alexandre Klementiev. 2012. A Bayesian approach to unsupervised semantic role induction. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 12–22, Avignon. find
  97. Toutanova, Kristina, Aria Haghighi, and Christopher Manning. 2005. Joint learning improves semantic role labeling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 589–596, Ann Arbor, MI. find
  98. Turian, Joseph, Lev-Arie Ratinov, and Yoshua Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 384–394, Uppsala. find our card
  99. Weston, Jason, Frédéric Ratle, and Ronan Collobert. 2008. Deep learning via semi-supervised embedding. In Proceedings of the 25th International Conference on Machine Learning, pages 1,168–1,175, Helsinki. find
  100. Xue, Nianwen and Martha Palmer. 2004. Calibrating features for semantic role labeling. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pages 88–94, Barcelona. find
  101. Yi, Szu-ting, Edward Loper, and Martha Palmer. 2007. Can semantic roles generalize across genres? In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 548–555, Rochester, NY. find
  102. Zhu, Xiaojin. 2008. Semi-supervised learning literature survey. Available at http://pages.cs.wisc.edu/ ∼ jerryzhu/ pub/ssl survey.pdf. ↗
  103. Last Accessed July 2013. find
  104. Zhu, Xiaojin, Zoubin Ghahramani, and John Lafferty. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, pages 912–919, Washington, DC. find