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FactBank: a corpus annotated with event factuality

Abstract

Recent work in computational linguistics points out the need for systems to be sensitive to the veracity or factuality of events as mentioned in text; that is, to recognize whether events are presented as corresponding to actual situations in the world, situations that have not happened, or situations of uncertain interpretation. Event factuality is an important aspect of the representation of events in discourse, but the annotation of such information poses a representational challenge, largely because factuality is expressed through the interaction of numerous linguistic markers and constructions. Many of these markers are already encoded in existing corpora, albeit in a somewhat fragmented way. In this article, we present FactBank, a corpus annotated with information concerning the factuality of events. Its annotation has been carried out from a descriptive framework of factuality grounded on both theoretical findings and data analysis. FactBank is built on top of TimeBank, adding to it an additional level of semantic information.

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Notes

  1. 1.

    The main references for these corpora are: PropBank (Palmer et al. 2005), FrameNet (Baker et al. 1998), RST Corpus (Carlson et al. 2003), Penn Discourse TreeBank (Miltsakaki et al. 2004), GraphBank (Wolf and Gibson 2005), TimeBank (Pustejovsky et al. 2006), MPQA Opinion Corpus (Wiebe et al. 2005).

  2. 2.

    In this article, the term event will be used in a very broad sense to refer to both processes and states, but also other abstract objects such as propositions, facts, possibilities, etc.

  3. 3.

    This is distinct from most of the work within truth-conditional semantics, which conceives of modality as independent from the speaker’s perspective (e.g., Kratzer 1991).

  4. 4.

    Here and throughout the rest of the article, events in the examples will be identified by marking only their verb, noun, or adjective head, together with polarity particles and auxiliaries when deemed necessary. This follows the convention assumed in TimeML, the specification language used to represent event and temporal information in the corpus presented here (Pustejovsky et al. 2006).

  5. 5.

    Some authors use the term hedging to refer to markers of modality expressing the degree of commitment of the source towards the certainty of a proposition. See, e.g., Clemen (1997).

  6. 6.

    See Saurí (2008) for a more comprehensive view on the factuality of events and its identification.

  7. 7.

    The original sentence in this set is (17b), from the British National Corpus.

  8. 8.

    Furthermore, Nairn et al. (2006), Saurí and Pustejovsky (2007), and Saurí (2008) show that the interaction among all these elements can be modeled in a predictable way.

  9. 9.

    This is equivalent to the notation < author,izvestiya > in Wiebe’s work. Here, we adopt a reversed representation of the nesting (i.e., the non-embedded source last) because it positions the most direct source of the event at the outmost layer, thus facilitating its reading.

  10. 10.

    From Rubin (2006, p. 59).

  11. 11.

    Scalar predications are conceived as collections of predicates P n such as <P j , P j−1, …, P2, P1>, where P n outranks (i.e., is stronger than) P n−1 on the relevant scale.

  12. 12.

    The vowels naming the vertices, which are derived from Latin verbs a ff i rmo ‘I affirm’, and n e g o ‘I deny’, reflect this distinction.

  13. 13.

    Semantically, this can be interpreted as: Val(mod,Val(pol,e))—i.e., the modal value scopes over the polarity value.

  14. 14.

    This step is applied here only for the purpose of illustrating the complete process, although it should be clear just from the meaning of the sentence that the event change in the original example is presented with some degree of uncertainty.

  15. 15.

    http://www.timeml.org/site/timebank/timebank.html.

  16. 16.

    The figures reported here update those reported in previous work (Saurí 2008; Saurí and Pustejovsky 2008).

  17. 17.

    TimeML has moved towards a stand-off annotation. The example here is embedded for illustration purposes.

  18. 18.

    It must be pointed out, however, that none of the aforementioned issues are problems from a TimeML perspective, since its goal is not to provide a full-fledged annotation of factuality. Moreover, TimeML has been intentionally conceived of as a surface-based markup, which explains why, for instance, modal auxiliaries are recorded but not interpreted.

  19. 19.

    For the sake of clarity, the example above provides both the form and the ID for events and sources, but the original FactBank annotation records only the IDs.

  20. 20.

    We follow here the same approach as TimeML of annotating only heads.

  21. 21.

    These syntactic functions were obtained from parsing the corpus with the Stanford Parser (de Marneffe et al. 2006b).

  22. 22.

    As a matter of fact, there was no event judged as such throughout the whole corpus.

  23. 23.

    Rubin’s approach and ours are not completely equivalent, since she annotates only sentences where there are “explicit markers of certainty”, whereas we assume that factuality is a value affecting all events in text. In addition, her system does not consider polarity as part of the information to identify.

References

  1. ACE (2008). ACE (Automatic Content Extraction) English annotation guidelines for relations (Version 6.0 – 2008.01.07 ed.). Linguistic Data Consortium. http://www.ldc.upenn.edu/Projects/ACE.

  2. Aikhenvald, A. Y. (2004). Evidentiality. Oxford, England: Oxford University Press.

    Google Scholar 

  3. Andreevskaia, A., & Bergler, S. (2006). Mining WordNet for fussy sentiment: Sentiment tag extraction from WordNet glosses. In Proceedings of the 11th conference of the European chapter of the Association for the Computational Linguistics, EACL-2006.

  4. Asher, N. (1993). Reference to abstract objects in English. Dordrecht, The Netherlands: Kluwer Academic Press.

    Google Scholar 

  5. Bach, K., & Harnish, R. M. (1979). Linguistic communication and speech acts. Cambridge, Massachusetts, USA: The MIT Press.

    Google Scholar 

  6. Baker, C. F., Fillmore, C. J., & Lowe, J. B. (1998). The Berkeley FrameNet project. In 17th International conference on computational linguistics (pp. 86–90).

  7. Bergler, S. (1992). Evidential analysis of reported speech. PhD thesis, Brandeis University.

  8. Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., & Jurafsky, D. (2004). Automatic extraction of opinion propositions and their holders. In Proceedings of AAAI spring symposium on exploring attitude and affect in text.

  9. Biber, D., & Finegan, E. (1989). Styles of stance in English: Lexical and grammatical marking of evidentiality and affect. Text, 9(1), 93–124.

    Google Scholar 

  10. Carlson, L., Marcu, D., & Okurowski, M. E. (2003). Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In J. v. Kuppevelt & R. W. Smith (Eds.), Current and new directions in discourse and dialogue. Springer.

  11. Chafe, W. (1986). Evidentiality in English conversation and academic writing. In W. Chafe & J. Nichols (Eds.), Evidentiality: The linguistic coding of epistemology. Norwood, New Jersey, USA: Ablex Publishing Corporation.

    Google Scholar 

  12. Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005). Identifying sources of opinions with conditional random fields and extraction patterns. In Proceedings of the HLT/EMNLP 2005. Vancouver, Canada.

  13. Clemen, G. (1997). The concept of hedging: Origins, approaches and definitions. In R. Markkanen & H. Schröder (Eds.), Hedging and discourse: Approaches to the analysis of a pragmatic phenomenon in academic texts (pp. 235–248). Berlin; New York: Walter de Gruyter.

    Google Scholar 

  14. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 10, 37–46.

    Article  Google Scholar 

  15. Condoravdi, C., Crouch, R., van den Berg, M., Everett, J., Stolle, R., Paiva, V., & Bobrow, D. (2001). Preventing existence. In Proceedings of the conference on formal ontologies in information systems (FOIS), Ogunquit, Maine, USA.

  16. Dave, K. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of World Wide Web conference 2003.

  17. de Haan, F. (1997). The interaction of modality and negation: A typological study. New York, USA: Garland.

    Google Scholar 

  18. de Haan, F. (2000). The relation between modality and evidentiality. In R. Müller & M. Reis (Eds.), Modalität und Modalverben im Deutschen. Hamburg, Germany: Helmut Buske Verlag.

    Google Scholar 

  19. de Marneffe, M.-C., MacCartney, B., Grenager, T., Cer, D., Rafferty, A., & Manning, C. D. (2006a). Learning to distinguish valid textual entailments. In Second PASCAL RTE Challenge (RTE-2).

  20. de Marneffe, M.-C., MacCartney, B., & Manning, C. D. (2006b). Generating typed dependency parses from phrase structure parses. In Proceedings of LREC 2006.

  21. Di Eugenio, B., & Glass, M. (2004). The kappa statistic: a second look. Computational Linguistics, 30, 95–101.

    Google Scholar 

  22. Dor, D. (1995). Representations, attitudes and factivity evaluations. An epistemically-based analysis of lexical selection. PhD thesis, Stanford University.

  23. Geurts, B. (1998). Presuppositions and anaphors in attitude contexts. Linguistics and Philosophy, 21, 545–601.

    Article  Google Scholar 

  24. Givón, T. (1993). English grammar. A function-based introduction. Amsterdam, The Netherlands: John Benjamins.

    Google Scholar 

  25. Glanzberg, M. (2003). Felicity and presupposition triggers. In University of Michigan Workshop in Philosophy and Linguistics. Michigan, USA.

  26. Halliday, M. A. K. (1994). An introduction to Functional Grammar (2nd ed.). London, England: Edward Arnold.

    Google Scholar 

  27. Halliday, M. A. K., & Matthiessen, C. M. (2004). An introduction to Functional Grammar. London, England: Hodder Arnold.

    Google Scholar 

  28. Hickl, A., & Bensley, J. (2007). A discourse commitment-based framework for recognizing textual entailment. In Proceedings of the workshop on textual entailment and paraphrasing (pp. 171–176). Prague, Czech Republic.

  29. Hooper, J. B. (1975). On assertive predicates. In J. Kimball (Ed.), Syntax and semantics, IV (pp. 91–124). New York, USA: Academic Press.

    Google Scholar 

  30. Horn, L. R. (1972). On the semantic properties of logical operators in English. PhD thesis, UCLA. Distributed by the Indiana University Linguistics Club in 1976.

  31. Horn, L. R. (1989). A natural history of negation. Chicago, USA: University of Chicago Press.

    Google Scholar 

  32. Huddleston, R. (1984). Introduction to the grammar of English. Cambridge, England: Cambridge University Press.

    Google Scholar 

  33. Karttunen, L. (1970). Implicative verbs. Language, 47, 340–358.

    Article  Google Scholar 

  34. Karttunen, L. (1973). Presuppositions of compound sentences. Linguistic Inquiry, 4(2), 169–193.

    Google Scholar 

  35. Karttunen, L., & Zaenen, A. (2005). Veridicity. In G. Katz, J. Pustejovsky, & F. Schilder (Eds.), Dagstuhl seminar proceedings. Schloss Dagstuhl, Germany. Internationales Begegnungs- und Forschungszentrum (IBFI).

  36. Kiefer, F. (1987). On defining modality. Folia Linguistica, XXI, 67–94.

    Article  Google Scholar 

  37. Kiparsky, P., & Kiparsky, C. (1970). Fact. In M. Bierwisch & K. E. Heidolph (Eds.), Progress in linguistics. A collection of papers (pp. 143–173). The Hague: Mouton.

  38. Koenig, J.-P., & Davis, A. R. (2001). Sublexical modality and the structure of lexical semantics. Linguistics and Philosophy, 24, 71–124.

    Article  Google Scholar 

  39. Kratzer, A. (1991). Modality. In A. van Stechow & D. Wunderlich (Eds.), Semantik: Ein internationales Handbuch der zeitgenoessischen Forschung (pp. 639–650). Berlin, Germany: Walter de Gruyter.

    Google Scholar 

  40. Light, M., Qiu, X. Y., & Srinivasan, P. (2004). The language of Bioscience: Facts, speculations, and statements in between. In BioLINK 2004: Linking biological literature, ontologies, and databases (pp. 17–24).

  41. Lyons, J. (1977). Semantics. Cambridge, England: Cambridge University Press.

    Google Scholar 

  42. Martin, J. R., & White, P. R. R. (2005). Language of evaluation: Appraisal in English. Palgrave Macmillan.

  43. Meyers, A., Reeves, R., Macleod, C., Szekely, R., Zielinska, V., Young, B., & Grishman, R. (2004). The NomBank project: An interim report. In Proceedings of frontiers in corpus annotation workshop. HLT-NAACL.

  44. Miltsakaki, E., Prasad, R., Joshi, A., & Webber, B. (2004). The Penn Discourse TreeBank. In Proceedings of LREC 2004.

  45. Mushin, I. (2001). Evidentiality and epistemological stance. Amsterdam/Philadelphia: John Benjamin.

    Google Scholar 

  46. Nairn, R., Condoravdi, C., & Karttunen, L. (2006). Computing relative polarity for textual inference. In Inference in Computational Semantics, ICoS-5.

  47. Palmer, F. R. (1986). Mood and modality. Cambridge, England: Cambridge University Press.

    Google Scholar 

  48. Palmer, M., Gildea, D., & Kingsbury, P. (2005). The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1), 71–105.

    Google Scholar 

  49. Pang, B., & Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the ACL, 115–124.

  50. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the EMNLP 2002.

  51. Polanyi, L., & Zaenen, A. (2005). Contextual lexical valence shifters. In J. Shanahan, Y. Qu, & J. Wiebe (Eds.), Computing attitude and affect in text: Theories and applications. New York, NY, USA: Springer-Verlag.

    Google Scholar 

  52. Pradhan, S., Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., & Weischedel, R. (2007). OntoNotes: A unified relational semantic representation. In Proceedings of IEEE international conference on semantic computing, ICSC 2007 (pp. 517–526).

  53. Prasad, R., Dinesh, N., Lee, A., Joshi, A., & Webber, B. (2007). Attribution and its annotation in the Penn Discourse TreeBank. Traitement Automatique des Langues, 47(2), 43–63.

    Google Scholar 

  54. Prasad, R., Dinesh, N., Lee, A., Robaldo, L., Joshi, A., & Webber, B. (2008). The Penn Discourse TreeBank 2.0. In Proceedings of LREC 2008, Marrakesh, Morocco.

  55. Pustejovsky, J., Castano, J., Ingria, R., Saurí, R., Gaizauskas, R., Setzer, A., & Katz, G. (2003). TimeML: Robust specification of event and temporal expressions in text. In IWCS-5, fifth international workshop on computational semantics.

  56. Pustejovsky, J., Verhagen, M., Saurí, R., Littman, J., Gaizauskas, R., Katz, G., Mani, I., Knippen, R., & Setzer, A. (2006). TimeBank 1.2. Linguistic Data Consortium (LDC). Philadelphia, PA. http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2006T08.

  57. Pustejovsky, J., Knippen, B., Littman, J., & Saurí, R. (2005). Temporal and event information in natural language text. Language Resources and Evaluation, 39(2), 123–164.

    Article  Google Scholar 

  58. Pustejovsky, J., & Rumshisky, A. (2008). Between chaos and structure: Interpreting lexical data through a theoretical lens. Special Issue of International Journal of Lexicography in Memory of John Sinclair, 21(3), 337–355.

    Google Scholar 

  59. Quirk, R., Greenbaum, S., Leech, G., & Svartik, J. (1985). A comprehensive grammar of the English language. London, England: Longman.

    Google Scholar 

  60. Read, J., Hope, D., & Carroll, J. (2007). Annotating expressions of appraisal in English. In Proceedings of the linguistic annotation workshop, Prague. Association for Computational Linguistics, ACL.

  61. Riloff, E., Wiebe, J., & Wilson, T. (2003). Learning subjective nouns using extraction pattern bootstrapping. In Proceedings of the 7th conference on natural language learning (CoNLL 2003).

  62. Rubin, V. L. (2006). Identifying certainty in texts. PhD thesis, Syracuse University.

  63. Rubin, V. L. (2007). Stating with certainty or stating with doubt: Intercoder reliability results for manual annotation of epistemically modalized statements. In Proceedings of the NAACL-HLT 2007.

  64. Rubin, V. L., Liddy, E. D., & Kando, N. (2005). Certainty identification in texts: Categorization model and manual tagging results. In J. Shanahan, Y. Qu, & J. Wiebe (Eds.), Computing attitude and affect in text: Theories and applications. New York, USA: Springer-Verlag.

    Google Scholar 

  65. Saurí, R. (2008). A factuality profiler for eventualities in text. PhD thesis, Brandeis University.

  66. Saurí, R., & Pustejovsky, J. (2007). Determining modality and factuality for text entailment. In Proceedings of the first IEEE international conference on semantic computing, Irvine, CA, USA.

  67. Saurí, R., & Pustejovsky, J. (2008). From structure to interpretation: A double-layered annotation for event factuality. In Proceedings of the second linguistic annotation workshop (The LAW II). LREC 2008, Marrakesh, Morocco.

  68. Saurí, R., Verhagen, M., & Pustejovsky, J. (2006a). Annotating and recognizing event modality in text. In 19th International FLAIRS conference, FLAIRS 2006. The Florida Artificial Intelligence Research Society.

  69. Saurí, R., Verhagen, M., & Pustejovsky, J. (2006b). SlinkET: A partial modal parser for events. In Proceedings of LREC 2006, Genoa, Italy.

  70. Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics for the behavioral sciences. Boston, MA, USA: McGraw Hill.

    Google Scholar 

  71. Snow, R., & Vanderwende, L. (2006). Effectively using syntax for recognizing false entailment. In Proceedings of HLT-NAACL 2006.

  72. Stoyanov, V., & Cardie, C. (2008). Annotating topics of opinions. In Proceedings of LREC 2008, Marrakech, Morocco. ELDA.

  73. Tatu, M., & Moldovan, D. (2005). A semantic approach to recognizing textual entailment. In Proceedings of HLT/EMNLP (pp. 371–378).

  74. Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th ACL, 417–424.

  75. Van Valin, R. D., & LaPolla, R. J. (1997). Syntax. Structure, meaning and function. Cambridge, England: Cambridge University Press.

    Google Scholar 

  76. Verhagen, M., Stubbs, A., & Pustejovsky, J. (2007). Combining independent syntactic and semantic annotation schemes. In Proceedings of the Linguistic Annotation Workshop (pp. 109–112). ACL. Prague, Czech Republic.

  77. Waugh, L. R. (1995). Reported speech in journalistic discourse: The relation of function and text. Text, 15(1), 129–173.

    Google Scholar 

  78. Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2), 165–210.

    Article  Google Scholar 

  79. Wiebe, J. M. (2000). Learning subjective adjectives from corpora. In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI 2000).

  80. Wierzbicka, A. (1987). English speech act verbs. A semantic dictionary. Sydney, Australia: Academic Press.

    Google Scholar 

  81. Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005). OpinionFinder: A system for subjectivity analysis. In Proceedings of the HLT/EMNLP 2005 Demonstration Abstracts (pp. 34–35). Vancouver, Canada.

  82. Wilson, T., Wiebe, J., & Hwa, R. (2004). Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of the 19th National Conference on Artificial Intelligence (AAAI 2004).

  83. Wolf, F., & Gibson, E. (2005). Representing discourse coherence: A corpus-based analysis. Computational Linguistics, 31(2), 249–287.

    Article  Google Scholar 

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Acknowledgments

We are very grateful to Marc Verhagen, Toni Badia, Lauri Karttunen, Rick Alterman, Sabine Bergler, Adam Meyers, and Silvia Pareti for their valuable comments and helpful discussion regarding this research. We also want to extend thanks to four anonymous reviewers for their constructive suggestions, which helped improve the original manuscript. All errors and mistakes are responsibility of the authors. This work is been supported by a grant to Prof. Pustejovsky, NAVAIR Contract No. N61339-06-C-0140.

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Saurí, R., Pustejovsky, J. FactBank: a corpus annotated with event factuality. Lang Resources & Evaluation 43, 227 (2009). https://doi.org/10.1007/s10579-009-9089-9

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Keywords

  • Event factuality
  • Modality
  • Certainty
  • Subjectivity analysis
  • Corpus creation
  • TimeBank