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FrameNet: Frame Semantic Annotation in Practice

  • Collin F. BakerEmail author
Chapter

Abstract

Beginning with an overview of the theory of Frame Semantics as developed by Charles Fillmore and colleagues, this article details the annotation of English sentences by the FrameNet Project based on this theory. Fillmore’s lexical semantics theory asserts that the meanings of most words are understood via the semantic frames they evoke; e.g. arrest, apprehend, apprehension, bust, and nab can all evoke the Arrest frame, with its associated frame-specific semantic roles: Suspect, Authorities, Offense, and Charges. Thus, They were busted for shoplifting by three plainclothes policemen would be labeled to show that bust is the frame-evoking expression, they fills the Suspect role, for shoplifting is the Offense, and by three plainclothes policemen represents the Authorities. Combining multiple annotations of this type creates a picture of the valence (valency) patterns of the lexical unit (word sense) and the semantic frame. The resulting database contains more than 200,000 manual annotations of 13,500 lexical units in 1,200 semantic frames. Expanding from the original goal of lexicography, the team has annotated a number of texts “fully”, i.e. labeling all the frame-evoking elements and the phrases that fill their semantic roles, providing a rich representation of the lexical semantics of the entire text. Automatic semantic role labeling systems trained on FrameNet can label a wide range of texts with increasing accuracy for NLP research and applications. The author describes current limitations and possible extensions of this methodology and how the practice of manual annotation informs the development of the theory.

Keywords

Frame semantics Lexical semantics Manual annotation Valency Lexicography Semantic roles 

Notes

Acknowledgements

The author would like to acknowledge the extremely helpful comments from two reviewers, who pointed out many places where the text was not clear; any remaining lack of clarity, errors and omissions are entirely the author’s.

The FrameNet Project got underway thanks to two NSF grants, IRI #9618838, “Tools for Lexicon Building” (PIs Fillmore and Dan Jurafsky) and ITR/HCI #0086132, “FrameNet ++: An On-Line Lexical Semantic Resource and its Application to Speech and Language Technology” (PIs Fillmore, Jurafsky, Srini Narayanan, and Mark Gawron), which funded frame semantic research at ICSI 1997–2000 and 2000–2003, respectively. We also gratefully acknowledge a series of grants from NSF (IIS-0535297), ARDA AQUAINT 2005–2006, DARPA 2003–2005 (FA8750-04-2-0026), NSF 2000-2004 (ITR/HCI 0086132) and NSF 2006-present (IIS-0535297, 0705155, 0708952, 0855271, 0947841 and CNS-1406048). FrameNet is also grateful for subcontracts with Decisive Analytics, Inc., as well as a research fellowship from Google, Inc.

References

  1. 1.
    Annesi, P., Basili, R.: Cross-Lingual Alignment of FrameNet Annotations through Hidden Markov Models. In: Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing, CICLing’10 Alexander Gelbukh (ed.). Lecture Notes in Computer Science, 12–25, vol. 6008. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Baker, C., Ellsworth, M., Erk, K.: SemEval-2007 task 19: frame semantic structure extraction. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Association for Computational Linguistics, pp. 99–104, Prague, Czech Republic (2007)Google Scholar
  3. 3.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Boitet, C., Whitelock, P. (eds.) Proceedings of the Thirty-Sixth Annual Meeting of the Association for Computational Linguistics and Seventeenth International Conference on Computational Linguistics, pp. 86–90. California. Morgan Kaufmann Publishers, San Francisco (1998)Google Scholar
  4. 4.
    Bergen, B.: Louder than Words: The New Science of How the Mind Makes Meaning. Basic Books, New York (2012)Google Scholar
  5. 5.
    Bertoldi, A., Chishman, R.L.O.: Developing a frame-based Lexicon for the Brazilian legal language: The Case of the Criminal Process FrameNet. In: Palmirani, M., Pagallo, U., Casanovas, P., Sartor, G. (eds.) AI Approaches to the Complexity of Legal Systems. Models and Ethical Challenges for Legal Systems, Legal Language and Legal Ontologies, Argumentation and Software Agents. Lecture Notes in Computer Science, vol. 7639, pp. 256–270. Springer, Heidelberg (2012)Google Scholar
  6. 6.
    Boas, H.C.: (ed.) Multilingual FrameNets in Computational Lexicography: Methods and Applications. Mouton de Gruyter (2009)Google Scholar
  7. 7.
    Bonial, C., Stowe, K., Palmer, M.: Renewing and revising SemLink. In: Proceedings of he GenLex Workshop on Linked Data in Linguistics (GenLex-13). Pisa, Italy (2013)Google Scholar
  8. 8.
    Borin, L., Danélls, D., Forsberg, M., Kokkinakis, D., Gronostaj, M.T.: The past meets the present in Swedish FrameNet++. In: Proceedings of EURALEX 14, pp. 269–281. EURALEX (2010)Google Scholar
  9. 9.
    Bryl, V., Tonelli, S., Giuliano, C., Serafini, L.: A novel FrameNet-based resource for the semantic web. In: Proceedings of ACM Symposium on Appliced Computing (SAC), Riva del Garda (Trento), ItalyGoogle Scholar
  10. 10.
    Burchardt, A., Erk, K., Frank, A.: A WordNet detour to FrameNet. In: Sprachtechnologie, mobile Kommunikation und linguistische Resourcen, Computer Studies in Language and Speech, vol. 8 (2005)Google Scholar
  11. 11.
    Burchardt, A., Pennachiotti, M., Thater, S., Pinkal, M.: Assessing the impact of frame semantics on textual entailment. Nat. Lang. Eng. 15, 527–550 (2009)CrossRefGoogle Scholar
  12. 12.
    Burnard, L.: User’s guide for the British National Corpus. Oxford University Computing Services, British National Corpus Consortium (1995)Google Scholar
  13. 13.
    Burnard, L., Aston, G.: The BNC Handbook: Exploring the British National Corpus with SARA. Edinburgh University Press, Edinburgh (1998). http://www.natcorp.ox.ac.uk/
  14. 14.
    Carreras, X., Màrquez, L.: Introduction to the CoNLL-2004 shared task: semantic role labeling. In: Ng, H.T., Ellen Riloff, E. (eds.), HLT-NAACL 2004 Workshop: Eighth Conference on Computational Natural Language Learning (CoNLL-2004), Association for Computational Linguistics, pp. 89–97. Boston, Massachusetts, USA (2004)Google Scholar
  15. 15.
    Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Association for Computational Linguistics, pp. 152–164, Ann Arbor, Michigan (2005)Google Scholar
  16. 16.
    Chen, B., Fung, P.: Automatic Construction of an English–Chinese Bilingual FrameNet. In: HLT/NAACL: Proceedings. Boston (2004)Google Scholar
  17. 17.
    Chow, I.C., Webster, J.J.: Mapping FrameNet and SUMO with WordNet verb: statistical distribution of lexical-ontological realization. In: Mexican International Conference on Artificial Intelligence 0.262–268 (2006)Google Scholar
  18. 18.
    Coppola, B., Moschitti, A.: A general purpose FrameNet-based shallow semantic parser. In: Calzolari, N., (Conference Chair), Choukri, K., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S., Rosner, M., Tapias, D.: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10), Valletta, Malta. European Language Resources Association (ELRA) (2010)Google Scholar
  19. 19.
    Coppola, B., Moschitti, A., Riccardi, G.: Shallow semantic parsing for spoken language understanding. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, Association for Computational Linguistics, pp. 85–88. Boulder, Colorado (2009)Google Scholar
  20. 20.
    Dancygier, B., Sweetser, E.: Mental Spaces in Grammar: Conditional Constructions. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  21. 21.
    Das, D., Smith, N.A.: Semi-supervised frame-semantic parsing for unknown predicates. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 1435–1444. Portland, Oregon, USA (2011)Google Scholar
  22. 22.
    Das, D., Schneider, N., Chen, D., Smith, N.A.: SEMAFOR 1.0: A Probabilistic Frame-Semantic Parser. Technical Report CMU-LTI-10-001, Language Technologies Institute Carnegie Mellon University (2010)Google Scholar
  23. 23.
    Das, D., Chen, D., Martins, A.F.T., Schneider, N., Smith, N.A.: Frame-semantic parsing. Comput. Linguist. 40 (2013)Google Scholar
  24. 24.
    Dowty, D.R.: Thematic proto-roles and argument selection. Language 67, 547–619 (1991)CrossRefGoogle Scholar
  25. 25.
    Emele, M., Heid, U.: DELIS: Tools for corpus-based lexicon building. In: Proceedings of Konvens-94. Springer, Heidelberg (1994)Google Scholar
  26. 26.
    Erk, K., McCarthy, D.: Graded word sense assignment. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 440–449, Singapore (2009)Google Scholar
  27. 27.
    Erk, K., Padó, S.: Shalmaneser – a flexible toolbox for semantic role assignment. In: Proceedings of the fifth International Conference on Language Resources and Evaluation (LREC-2006). Genoa, Italy (2006)Google Scholar
  28. 28.
    Erk, K., Kowalski, A., Padó, S., Pinkal, M.: Towards a resource for lexical semantics: a large German corpus with extensive semantic annotation. In: Hinrichs, E., Roth, D. (eds.), Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 537–544 (2003)Google Scholar
  29. 29.
    Fauconnier, G., Turner, M.: Blending as a central process of grammar. In: Goldberg, A. (ed.) Conceptual Structure, Discourse and Language, pp. 113–130. CSLI Publications, Stanford (1996)Google Scholar
  30. 30.
    Fellbaum, C.: (ed.), WordNet. An Electronic Lexical Database. MIT Press, Cambridge (1998)Google Scholar
  31. 31.
    Ferrández, Ó., Ellsworth, M., Muñoz, R., Baker, C.F.: Aligning FrameNet and WordNet based on semantic neighborhoods. In: Calzolari, N., (Conference Chair), Choukri, K., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S., Rosner, M., Tapias, D. (eds.), Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10), pp. 310–314. Valletta, Malta. European Language Resources Association (ELRA) (2010)Google Scholar
  32. 32.
    Fillmore, C.J.: The case for case. In: Bach, E., Harms, R. (eds.), Universals in Linguistic Theory. Holt, Rinehart & Winston, New York (1968)Google Scholar
  33. 33.
    Fillmore, C.J.: Toward a modern theory of case. In: Reibel, D.A., Shane, S.A. (eds.) Modern Studies in English: Readings in Transformational Grammar, pp. 361–375. Prentice-Hall, Englewood Cliffs, New Jersey (1969)Google Scholar
  34. 34.
    Fillmore, C.J.: Frame semantics and the nature of language. Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech 280, 20–32 (1976)CrossRefGoogle Scholar
  35. 35.
    Fillmore, C.J.: Scenes-and-frames semantics. In: Zampolli, A. (ed.), Linguistic Structures Processing, Fundamental Studies in Computer Science, vol. 59. North Holland Publishing (1977)Google Scholar
  36. 36.
    Fillmore, C.J.: Frame semantics. In: Linguistics in the Morning Calm, pp. 111–137. Hanshin Publishing Co, Seoul, South Korea (1982)Google Scholar
  37. 37.
    Fillmore, C.J.: Frames and the semantics of understanding. Quaderni di Semantica 6, 222–254 (1985)Google Scholar
  38. 38.
    Fillmore, C.J.: Corpus linguistics versus computer-aided armchair linguistics. In: Directions in Corpus Linguistics: Proceedings from a 1991: Nobel Symposium on Corpus Linguistics, 35–66. Stockholm, Mouton de Gruyter (1992)Google Scholar
  39. 39.
    Fillmore, C.J.:, Atkins, B.T.S.: Towards a frame-based lexicon: The semantics of RISK and its neighbors. In: [54], 75–102 (1992)Google Scholar
  40. 40.
    Fillmore, C.J., Atkins, B.T.S.: Starting where the dictionaries stop: The challenge for computational lexicography. In: Zampolli, A., Atkins, s. (eds.), Computational Approaches to the Lexicon. Oxford University Press, Oxford (1994)Google Scholar
  41. 41.
    Fillmore, C.J., Baker, C.F.: A frames approach to semantic analysis. In: Heine, B., Narrog, H. (eds.), Oxford Handbook of Linguistic Analysis, pp. 313–341. OUP (2010)Google Scholar
  42. 42.
    Fossati, M., Giuliano, C., Tonelli, S.: Outsourcing FrameNet to the crowd. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (vol. 2: Short Papers), Association for Computational Linguistics, pp. 742–747, Sofia, Bulgaria (2013)Google Scholar
  43. 43.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. In: ACL 2000: Proceedings of ACL 2000. Hong Kong (2000)Google Scholar
  44. 44.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles: Gildea, Daniel, Jurafsky, D. Comput. Linguist. 28, 245–288 (2002)Google Scholar
  45. 45.
    Giuglea, A-M., Moschitti, A.: Semantic role labeling via FrameNet, VerbNet and PropBank. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 929–936, Sydney, Australia (2006)Google Scholar
  46. 46.
    Gurevych, I., Judith, E-K., Hartmann, S., Matuschek, M., Meyer, C.M., Wirth, C.: UBY - a large-scale unified lexical-semantic resource based on LMF. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), Association for Computational Linguistics, pp. 580–590, Avignon, France (2012)Google Scholar
  47. 47.
    Hacioglu, K.: Semantic role labeling using dependency trees. In: Proceedings of COLING-2004 (2004)Google Scholar
  48. 48.
    Hajič, J., Ciaramita, M., Johansson, R., Kawahara, D., Martí, M.A., Màrquez, L., Meyers, A., Nivre, J., Padó, S., Štěpánek, J., Straňák, P., Surdeanu, M., Xue, N., Zhang, Y.: The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task, Association for Computational Linguistics, pp. 1–18. Boulder, Colorado (2009)Google Scholar
  49. 49.
    Heid, U.: Relating lexicon and corpus: Computational support for corpus-based lexicon building in DELIS. In:  Martin, W., Meijs, W., Moerland, M., Pas, E.t., van Sterkenburg, P., Vossen, P. (eds.), EURALEX 1994 Proceedings. Vrije Universiteit, Amsterdam (1994)Google Scholar
  50. 50.
    Ide, N., Reppen, R., Suderman, K.: The American National Corpus: more than the web can provide. In: Proceedings of the Third Language Resources and Evaluation Conference (LREC), pp. 839–44. Las Palmas, Canary Islands, Spain (2002)Google Scholar
  51. 51.
    Johansson, R., Nugues, P.: A FrameNet-based semantic role labeler for Swedish. In: Proceedings of Coling/ACL 2006, Sydney, Australia (2006)Google Scholar
  52. 52.
    Johansson, R., Nugues, P.: LTH: semantic structure extraction using nonprojective dependency trees. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Association for Computational Linguistics, pp. 227–230. Prague, Czech Republic (2007)Google Scholar
  53. 53.
    Koeva, S.: Lexicon and grammar in Bulgarian FrameNet. In: Calzolari, N., (Conference Chair), Choukri, K., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S., Rosner, M., Tapias, D. (eds.), Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10), Valletta, Malta. European Language Resources Association (ELRA) (2010)Google Scholar
  54. 54.
    Lehrer, A., Kittay, E.F.: (eds.) Frames, Fields, and Contrasts: New Essays in Semantic and Lexical Organization. Lawrence Erlbaum Associates (1992)Google Scholar
  55. 55.
    Lenci, A., Johnson, M., Lapesa, G.: Building an Italian FrameNet through semi-automatic corpus analysis. In: Calzolari, N., (Conference Chair), Choukri, K., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S., Rosner, M., Tapias, D. (eds), Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10), Valletta, Malta. European Language Resources Association (ELRA) (2010)Google Scholar
  56. 56.
    Litkowski, K.: Senseval-3 task: automatic labeling of semantic roles. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 9–12. Spain. Association for Computational Linguistics, Barcelona (2004)Google Scholar
  57. 57.
    Lönneker-Rodman, B., Baker, C.F.: The FrameNet model and its applications. Nat. Lang. Eng. 15, 415–453 (2009)CrossRefGoogle Scholar
  58. 58.
    Loper, E., Bird, S.: NLTK: The Natural Language Toolkit. In: Proceedings, I. (ed.) of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Association for Computational Linguistics, Philadelphia (2002)Google Scholar
  59. 59.
    Lowe, J.B., Baker, C.F., Fillmore, C.J.: A Frame-Semantic Approach to Semantic Annotation. Tagging Text with Lexical Semantics: Why, What, and How? Proceedings of the Workshop, pp. 18–24. Special Interest Group on the Lexicon, Association for Computational Linguistics (1997)Google Scholar
  60. 60.
    Moschitti, A., Morarescu, P., Harabagiu, S.: Open domain information extraction via automatic semantic labelling. In: proceedings of the 2003 Special Track on Recent Advances in Natural Language at the 16th International FLAIRS Conference. AAAI, Florida (2003)Google Scholar
  61. 61.
    Može, S.: Semantično Označevanje Slovenščine Po Modelu FrameNet “Semantic Annotation of Slovenian According to the FrameNet Model”. Ba (diploma) thesis, U of Ljubljana (2009)Google Scholar
  62. 62.
    Narayanan, S.: Moving right along: a computational model of metaphoric reasoning about events. In: Proceedings of the /National Conference on Artificial Intelligence (AAAI ’99), pp. 121–128. AAAI Press, Orlando, Florida (1999). http://www.icsi.berkeley.edu/~snarayan/met.ps
  63. 63.
    Ngai, G., Dekai, W., Carpuat, M., Wang, C.-S., Wang, C.-Y.: Semantic role labeling with boosting, SVMs, maximum entropy, SNOW, and decision lists. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 183–186. Association for Computational Linguistics, Barcelona, Spain (2004)Google Scholar
  64. 64.
    Ohara, K.: Semantic Annotations in Japanese FrameNet: Comparing Frames in Japanese and English. In: Calzolari, N., (Conference Chair), Choukri, K., Declerck, T., Dogan, M.U., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S. (eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), European Language Resources Association (ELRA). Istanbul, Turkey (2012)Google Scholar
  65. 65.
    Ovchinnikova, E., Montazeri, N., Alexandrov, T., Hobbs, J.R., McCord, M.C., Mulkar-Mehta, R.: Abductive reasoning with a large knowledge base for discourse processing. In: Proceedings of IWCS 2011, pp. 225–234. ACL, Curran Associates (2011)Google Scholar
  66. 66.
    Padó, S.: Cross-Lingual Annotation Projection Models for Role-Semantic Information. Saarland University dissertation. Published as Volume 21, Saarbrücken Dissertations in Computational Linguistics and Language Technology. German Research Center for Artificial Intelligence (DFKI) and Saarland University (2007). ISBN 978-3-933218-20-9Google Scholar
  67. 67.
    Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31, 71–106 (2005)CrossRefGoogle Scholar
  68. 68.
    Palmer, A., Moon, T., Baldridge, J., Erk, K., Campbell, E., Can, T.: Computational strategies for reducing annotation effort in language documentation. LiLT 3 (2010)Google Scholar
  69. 69.
    Petruck, M.R.L., Fillmore, C.J., Baker, C.F., Ellsworth, M., Ruppenhofer, J.: Reframing FrameNet Data. In: Williams, G., Vessier, S. (eds.) Proceedings of The 11th EURALEX International Congress, pp. 405–416. France, Lorient (2004)Google Scholar
  70. 70.
    Pustejovsky, J.: The Generative Lexicon. The MIT Press, Cambridge (1995)Google Scholar
  71. 71.
    Ruppenhofer, J., Ellsworth, M., Petruck, M.R.L., Johnson, C.R., Baker, C.F., Scheffczyk, J.: FrameNet II: Extended Theory and Practice. International Computer Science Institute. Distributed with the FrameNet data. Berkeley, California. (2016)Google Scholar
  72. 72.
    Ruppenhofer, J., Sporleder, C., Morante, R., Baker, C., Palmer, M.: SemEval-2010 task 10: linking events and their participants in discourse. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009). Association for Computational Linguistics, pp. 106–111. Boulder, Colorado (2010)Google Scholar
  73. 73.
    Scheffczyk, J., Baker, C.F., Narayanan, S.: Reasoning over Natural Language Text by Means of FrameNet and Ontologies. In: Huang, C.-R., Calzolari, N., Gangemi, A., Lenci, A., Oltramari, A., Prévot, L. (eds.), Ontology and the Lexicon: A Natural Language Processing Perspective, Studies in Natural Language Processing, Chap. 4, pp. 53–71. Cambridge University Press, Cambridge (2010). (Expanded version of paper at OntoLex, 2006. (ISBN-13: 9780521886598))Google Scholar
  74. 74.
    Siegel, S.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill (1956)Google Scholar
  75. 75.
    Sinha, S., Narayanan, S.: Model based answer selection. In: Proceedings of the Workshop on Textual Inference, 18th National Conference on Artificial Intelligence, AAAI, Pittsburgh (2005)Google Scholar
  76. 76.
    Subirats, C.: Spanish FrameNet: a frame-semantic analysis of the Spanish lexicon. In: Boas, H. (ed.) Multilingual FrameNets in Computational Lexicography: Methods and Applications, pp. 135–162. Mouton de Gruyter, Berlin/New York (2009)Google Scholar
  77. 77.
    Surdeanu, M., Johansson, R., Meyers, A., Marquez, L., Nivre, J.: The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. In: Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL-2008) (2008)Google Scholar
  78. 78.
    Sweetser, E.: Negative spaces: levels of negation and kinds of spaces. In: Bonnefille, S., Salbayre, S. (eds.) Proceedings of the conference “Negation: Form, figure of speech, conceptualization”. Tours. Groupe de recherches anglo-américaines de l’Université de Tours, Publications universitaires Fran cois Rabelais (2006)Google Scholar
  79. 79.
    Thompson, C., Levy, R., Manning, C.: A generative model for FrameNet semantic role labeling. In: Lavrac, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) Proceedings of the 14th European Conference on Machine Learning, Machine Learning: ECML 2003. Lecture Notes in Computer Science, vol. 2837, pp. 397–408. Springer, Cavtat-Dubrovnik, Croatia (2003)CrossRefGoogle Scholar
  80. 80.
    Tonelli, S.: Semi-automatic techniques for extending the FrameNet lexical database to new languages. Università Ca’ Foscari, Venezia dissertation (2010)Google Scholar
  81. 81.
    Tonelli, S., Pianta, E.: A novel approach to mapping FrameNet lexical units to WordNet synsets. In: Proceedings of IWCS-8. Tilburg, The Netherlands (2009)Google Scholar
  82. 82.
    Venturi, G., Lenci, A., Montemagn, S., Vecchi, E.M., Sagri, M.T., Tiscornia, D.: Towards a FrameNet resource for the legal domain. In: Proceedings of the Third Workshop on Legal Ontologies and Artificial Intelligence Techniques, Barcelona, Spain (2009)Google Scholar
  83. 83.
    Zipf, G.K.: Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Hafner Pub. Co, New York (1949). [1965]Google Scholar

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Authors and Affiliations

  1. 1.International Computer Science InstituteBerkeleyUSA

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