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FactBank: a corpus annotated with event factuality


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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  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.

  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.


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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).

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