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3. Temporal Relations

verfasst von : Leon R. A. Derczynski

Erschienen in: Automatically Ordering Events and Times in Text

Verlag: Springer International Publishing

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Abstract

Having discussed timex and events in the previous chapter, we move on to discuss the temporal relations that exist between them.

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Vorheriges Kapitel Events and Times

Nächstes Kapitel Relation Labelling Analysis

Although scale plays a part here; for some events, starting within the same week or even millennium can be considered synchronous, for others, picoseconds can be considered apart. The final choice is left to the annotator, who should interpret discourse accordingly.

Examined in greater detail in Sect. 3.3.2.

Available at http://sourceforge.net/projects/carafe/.

“GTag takes a document with TimeML tags, along with syntactic information from part-of-speech tagging and chunking from Carafe and then uses 187 syntactic and lexical rules to infer and label TLINKs between tagged events and other tagged events or times.” [21].

Setzer, A., Gaizauskas, R.: A pilot study on annotating temporal relations in text. In: Proceedings of the Workshop on Temporal and Spatial Information Processing, vol. 13, pp. 1–8. Association for Computational Linguistics (2001)

Setzer, A.: Temporal information in newswire articles: an annotation scheme and corpus study. Ph.D. thesis, The University of Sheffield (2001)

Allen, J.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)MATHCrossRef

Hobbs, J.R., Pan, F.: An ontology of time for the semantic web. ACM Trans. Asian Lang. Inf. Process. (TALIP) 3(1), 66–85 (2004)CrossRef

Prior, A.: Tense logic and the logic of earlier and later. Papers on Time and Tense, pp. 116–134 (1968)

Bruce, B.: A model for temporal references and its application in a question answering program. Artif. Intell. 3(1–3), 1–25 (1972)CrossRef

Moens, M., Steedman, M.: Temporal ontology and temporal reference. Comput. Linguist. 14(2), 15–28 (1988)

Goranko, V., Montanari, A., Sciavicco, G.: A road map of interval temporal logics and duration calculi. J. Appl. Nonclassical Log. 14(1/2), 9–54 (2004)MATHCrossRef

Denis, P., Muller, P.: Comparison of different algebras for inducing the temporal structure of texts. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 250–258. Association for Computational Linguistics (2010)

10.

Setzer, A., Gaizauskas, R., Hepple, M.: The role of inference in the temporal annotation and analysis of text. Lang. Res. Eval. 39(2), 243–265 (2005)CrossRef

11.

McDermott, D.: A temporal logic for reasoning about plans and actions. Cogn. Sci. 6, 101–155 (1982)CrossRef

12.

Galton, A.: Temporal logic. In: Zalta, E. (ed.) The Stanford Encyclopedia of Philosophy. Stanford University, The Metaphysics Research Lab (2008)

13.

Vilain, M., Kautz, H.: Constraint propagation algorithms for temporal reasoning. In: Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 377–382 (1986)

14.

Tsang, E.: The consistent labeling problem in temporal reasoning. In: Proceedings of the AAAI Conference, pp. 251–255. AAAI Press (1987)

15.

Allen, J.F., Hayes, P.J.: Moments and points in an interval-based temporal logic. Comput. Intell. 5(3), 225–238 (1989)CrossRef

16.

Freksa, C.: Temporal reasoning based on semi-intervals. Artif. Intell. 54(1), 199–227 (1992)MathSciNetCrossRef

17.

Kowalski, R., Sergot, M.: A logic-based calculus of events. In: Foundations Of Knowledge Base Management, pp. 23–55. Springer, Heidelberg (1989)

18.

Verhagen, M.: Times Between The Lines. Ph.D. thesis, Brandeis University (2004)

19.

Derczynski, L., Gaizauskas, R.: Analysing temporally annotated corpora with CAVaT. In: Proceedings of the Language Resources and Evaluation Conference, pp. 398–404 (2010)

20.

Denis, P., Muller, P.: Predicting globally-coherent temporal structures from texts via endpoint inference and graph decomposition. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (2011)

21.

Mani, I., Verhagen, M., Wellner, B., Lee, C., Pustejovsky, J.: Machine learning of temporal relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, p. 760. Association for Computational Linguistics (2006)

22.

Pustejovsky, J.: Personal correspondence (2009)

23.

Setzer, A., Gaizauskas, R.: Annotating events and temporal information in newswire texts. In: Proceedings of the Second International Conference On Language Resources And Evaluation (LREC-2000), vol. 31, Athens, Greece (2000)

24.

Mani, I., Wellner, B., Verhagen, M., Pustejovsky, J.: Three approaches to learning TLINKS in Time ML. Technical Report CS-07-268, Brandeis University, Waltham, MA, USA (2007)

25.

Pustejovsky, J., Stubbs, A.: Natural language annotation for machine learning. O’Reilly Media, Inc. (2012)

26.

Verhagen, M.: Temporal closure in an annotation environment. Lang. Res. Eval. 39(2), 211–241 (2005)CrossRef

27.

Chklovski, T., Pantel, P.: Verbocean: Mining the web for fine-grained semantic verb relations. Proc. EMNLP 4, 33–40 (2004)

28.

Chklovski, T., Pantel, P.: Path Analysis for Refining Verb Relations. In: Proceedings of KDD Workshop on Link Analysis and Group Detection (LinkKDD-04) (2004)

29.

Lin, D., Pantel, P.: Discovery of inference rules for question-answering. Natural Lang. Eng. 7(04), 343–360 (2002)

30.

Chambers, N., Jurafsky, D.: Unsupervised learning of narrative event chains. In: Proceedings the Annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics (2008)

31.

Boguraev, B., Pustejovsky, J., Ando, R., Verhagen, M.: Time Bank Evolution as a Community Resource for TimeML Parsing. Lang. Res. Eval. 41(1), 91–115 (2007)CrossRef

32.

Verhagen, M., Gaizauskas, R., Schilder, F., Hepple, M., Katz, G., Pustejovsky, J.: SemEval-2007 Task 15: TempEval: temporal relation identification. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval) (2007)

33.

Bethard, S., Martin, J., Klingenstein, S.: Timelines from Text: Identification of syntactic temporal relations. In: Proceedings of the International Conference on Semantic Computing, pp. 11–18 (2007)

34.

Tannier, X., Müller, P.: Evaluating temporal graphs built from texts via transitive reduction. J. Artif. Intell. Res. 40, 375–413 (2011)MATH

35.

Kelley Jr, J., Walker, M.: Critical-path planning and scheduling. AFIPS Joint Computer Conferences, pp. 160–173 (1959)

36.

Levenshtein, V.: Binary codes capable of correcting spurious insertions and deletions of ones. Probl. Inf. Trans. 1(1), 8–17 (1965)MathSciNetMATH

37.

UzZaman, N., Allen, J.: Temporal evaluation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 351–356. Association for Computational Linguistics (2011)

38.

Verhagen, M., Gaizauskas, R., Schilder, F., Hepple, M., Moszkowicz, J., Pustejovsky, J.: The TempEval challenge: identifying temporal relations in text. Lang. Res. Eval. 43(2), 161–179 (2009)CrossRef

39.

Verhagen, M., Saurí, R., Caselli, T., Pustejovsky, J.: SemEval-2010 Task 13: TempEval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 57–62. Association for Computational Linguistics (2010)

40.

UzZaman, N., Llorens, H., Derczynski, L., Verhagen, M., Allen, J., Pustejovsky, J.: SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations. In: Proceedings of the SemEval workshop. Association for Computational Linguistics (2013)

41.

Minard, A.L., Agirre, E., Aldabe, I., van Erp, M., Magnini, B., Rigau, G., Speranza, M., Urizar, R.: SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering. In: Proceedings of the workshop on Semantic Evaluation. Association for Computational Linguistics (2015)

42.

Llorens, H., Chambers, N., UzZaman, N., Mostafazadeh, N., Allen, J., Pustejovsky, J.: SemEval-2015 Task 5: QA TempEval. In: Proceedings of the workshop on Semantic Evaluation. Association for Computational Linguistics (2015)

43.

Bethard, S., Derczynski, L., Pustejovsky, J., Verhagen, M.: SemEval-2015 Task 6: Clinical TempEval. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Association for Computational Linguistics. Association for Computational Linguistics (2015)

44.

Popescu, O., Strapparava, C.: SemEval-2015 Task 7: Diachronic Text Evaluation. In: Proceedings of the workshop on Semantic Evaluation. Association for Computational Linguistics (2015)

45.

Boguraev, B., Ando, R.: TimeML-compliant text analysis for temporal reasoning. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) (2005)

46.

Zhang, T., Damerau, F., Johnson, D.: Text chunking based on a generalization of winnow. J. Mach. Learn. Res. 2, 615–637 (2002)MATH

47.

Hepple, M., Setzer, A., Gaizauskas, R.: USFD: preliminary exploration of features and classifiers for the TempEval-2007 tasks. In: Proceedings of the 4th International Workshop on Semantic Evaluations, SemEval ’07, pp. 438–441. Association for Computational Linguistics (2007)

48.

Pustejovsky, J., Hanks, P., Sauri, R., See, A., Gaizauskas, R., Setzer, A., Radev, D., Sundheim, B., Day, D., Ferro, L., et al.: The TimeBank Corpus. In: Proceedings of the Corpus Linguistics conference, pp. 647–656 (2003)

49.

ARDA: Aquaint timeml corpus (2006). http://www.timeml.org/site/timebank/timebank.html

50.

Mirroshandel, S., Ghassem-Sani, G., Khayyamian, M.: Using syntactic-based kernels for classifying temporal relations. J. Comput. Sci. Technol. 26(1), 68–80 (2010)CrossRef

51.

Chambers, N., Jurafsky, D.: Jointly combining implicit constraints improves temporal ordering. In: Proceedings of EMNLP, pp. 698–706. ACL (2008)

52.

Vasilakopoulos, A., Black, W.: Temporally ordering event instances in natural language texts. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2005) (2005)

53.

Mani, I., Schiffman, B., Zhang, J.: Inferring temporal ordering of events in news. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 55–57. Association for Computational Linguistics (2003)

54.

Reichenbach, H.: The tenses of verbs. Elements of Symbolic Logic. Dover Publications, New York (1947)

55.

Kolya, A., Ekbal, A., Bandyopadhyay, S.: Event-time relation identification using machine learning and rules. In: Text, Speech and Dialogue, pp. 117–124. Springer, Heidelberg (2011)

56.

Minsky, M.: Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine 12(2), 34 (1991)

57.

Kolya, A., Ekbal, A., Bandyopadhyay, S.: Event-event relation identification: a CRF based approach. In: 2010 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), pp. 1–8. IEEE (2010)

58.

Kolya, A., Ekbal, A., Bandyopadhyay, S.: JU_CSE_TEMP: A first step towards evaluating events, time expressions and temporal relations. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 345–350 (2010)

59.

Min, C., Srikanth, M., Fowler, A.: LCC-TE: A hybrid approach to temporal relation identification in news text. In: Proceedings of SemEval-2007, pp. 219–222. ACL (2007)

60.

Hagège, C., Tannier, X.: XRCE-T: XIP temporal module for TempEval campaign. In: Proceedings of the fourth international workshop on semantic evaluations (SemEval-2007), pp. 492–495 (2007)

61.

Yoshikawa, K., Riedel, S., Asahara, M., Matsumoto, Y.: Jointly identifying temporal relations with Markov logic. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 405–413. Association for Computational Linguistics (2009)

62.

Bethard, S., Martin, J., Klingenstein, S.: Finding temporal structure in text: machine learning of syntactic temporal relations. Int. J. Semant. Comput. 1(4), 441 (2007)CrossRef

63.

Cheng, Y., Asahara, M., Matsumoto, Y.: NAIST.Japan: temporal relation identification using dependency parsed tree. In: Proceedings of the 4th International Workshop on Semantic Evaluations, SemEval ’07, pp. 245–248. Association for Computational Linguistics (2007)

64.

UzZaman, N., Allen, J.: TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 276–283. Association for Computational Linguistics (2010)

65.

Marsic, G.: Temporal processing of news: Annotation of temporal expressions, verbal events and temporal relations. Ph.D. thesis, University of Wolverhampton (2011)

66.

Puşcaşu, G.: WVALI: Temporal relation identification by syntactico-semantic analysis. In: Proceedings of the 4th International Workshop on SemEval, vol. 2007, pp. 484–487 (2007)

67.

Ha, E., Baikadi, A., Licata, C., Lester, J.: NCSU: Modeling temporal relations with markov logic and lexical ontology. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval ’10, pp. 341–344. Association for Computational Linguistics (2010)

68.

Llorens, H., Saquete, E., Navarro, B.: TIPSem (English and Spanish): Evaluating CRFs and Semantic Roles in TempEval-2. In: Proceedings of the SemEval-2010, pp. 284–291. ACL (2010)

69.

Llorens, H., Saquete, E., Navarro-Colorado, B.: Automatic system for identifying and categorizing temporal relations in natural language. Int. J. Intell. Syst. 27, 680–708 (2012)CrossRef

70.

Derczynski, L., Gaizauskas, R.: Using signals to improve automatic classification of temporal relations. In: Proceedings of the ESSLLI StuS (2010)

71.

Derczynski, L., Gaizauskas, R.: USFD2: Annotating temporal expressions and TLINKs for TempEval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 337–340. Association for Computational Linguistics (2010)

72.

Bethard, S., Savova, G., Chen, W.T., Derczynski, L., Pustejovsky, J., Verhagen, M.: Semeval-2016 task 12: Clinical tempeval. In: Proceedings of SemEval pp. 1052–1062 (2016)

73.

Miller, T.A., Bethard, S., Dligach, D., Pradhan, S., Lin, C., Savova, G.K.: Discovering narrative containers in clinical text. ACL 2013, 18 (2013)

74.

Pustejovsky, J., Stubbs, A.: Increasing informativeness in temporal annotation. In: Proceedings of the 5th Linguistic Annotation Workshop, pp. 152–160. Association for Computational Linguistics (2011)

75.

Tatu, M., Srikanth, M.: Experiments with reasoning for temporal relations between events. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pp. 857–864. Association for Computational Linguistics (2008)

76.

Dang, H., Lin, J., Kelly, D.: Overview of the trec 2006 question answering track. In: Proceedings of the Text Retrieval and Evaluation Conference (2008)

77.

Howald, B., Katz, E.: On the explicit and implicit spatiotemporal architecture of narratives of personal experience. Spatial Information Theory, pp. 434–454 (2011)

78.

Ji, H., Grishman, R., Dang, H., Li, X., Griffit, K., Ellis, J.: Overview of the TAC2011 Knowledge Base Population Track. In: Proceedings of the Text Analytics Conference (2011)

79.

Burman, A., Jayapal, A., Kannan, S., Kavilikatta, M., Alhelbawy, A., Derczynski, L., Gaizauskas, R.: USFD at KBP 2011: Entity linking, slot filling and temporal bounding. In: Proceedings of the Text Analytics Conference (2011)

80.

Chambers, N., Wang, S., Jurafsky, D.: Classifying temporal relations between events. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 173–176. Association for Computational Linguistics (2007)

81.

Mirroshandel, S., Ghassem-Sani, G.: Temporal relation extraction using expectation maximization. In: Proceedings of RANLP (2011)

82.

Mirroshandel, S., Ghassem-Sani, G., Nasr, A.: Active learning strategies for support vector machines, application to temporal relation classification. In: Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 56–64 (2011)

83.

Mirroshandel, S., Ghassem-Sani, G.: Temporal relations learning with a bootstrapped cross-document classifier. In: Proceeding of the 19th European Conference on Artificial Intelligence, pp. 829–834 (2010)

84.

Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., Weischedel, R.: OntoNotes: the 90% solution. In: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pp. 57–60. Association for Computational Linguistics (2006)

85.

Lapata, M., Lascarides, A.: Learning sentence-internal temporal relations. J. Artif. Intell. Res. 27(1), 85–117 (2006)MATH

86.

Gaizauskas, R., Harkema, H., Hepple, M., Setzer, A.: Task-oriented extraction of temporal information: the case of clinical narratives. In: Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006, pp. 188–195. IEEE (2006)

87.

Bramsen, P., Deshpande, P., Lee, Y., Barzilay, R.: Finding temporal order in discharge summaries. In: AMIA Annual Symposium Proceedings, vol. 2006, p. 81. American Medical Informatics Association (2006)

88.

Bethard, S., Martin, J.: CU-TMP: temporal relation classification using syntactic and semantic features. In: Proceedings of the 4th International Workshop on Semantic Evaluations. SemEval ’07, pp. 129–132. Association for Computational Linguistics, Stroudsburg, PA, USA (2007)

89.

Costa, F., Branco, A.: Temporal relation classification based on temporal reasoning. In: Proceedings of the International Conference on Computational Semantics. Association for Computational Linguistics (2013)

90.

Hovy, D., Fan, J., Gliozzo, A., Patwardhan, S., Welty, C.: When did that happen?: linking events and relations to timestamps. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 185–193. Association for Computational Linguistics (2012)

91.

Laokulrat, N., Miwa, M., Tsuruoka, Y.: Exploiting timegraphs in temporal relation classification. In: TextGraphs-9 (2014)

92.

Puşcaşu, G.: Discovering temporal relations with TICTAC. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2007) (2007)

93.

Lee, C., Katz, G.: Error analysis of the TempEval temporal relation identification task. In: SEW-2009 Semantic Evaluations: Recent Achievements and Future Directions, pp. 138–145 (2009)

94.

Velupillai, S., Mowery, D.L., Abdelrahman, S., Christensen, L., Chapman, W.W.: BluLab: Temporal information extraction for the2015 clinical TempEval challenge. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Association for Computational Linguistics. Association for Computational Linguistics (2015)

95.

Scheuermann, A., Motta, E., Mulholland, P., Gangemi, A., Presutti, V.: An empirical perspective on representing time. In: Proceedings of the seventh international conference on Knowledge capture, pp. 89–96. ACM (2013)

96.

Derczynski, L.: Representation and Learning of Temporal Relations. Proceedings of the 26th International Conference on Computational Linguistics. Association for Comptuational Linguistics (2016)

Titel: Temporal Relations
verfasst von: Leon R. A. Derczynski
Verlag: Springer International Publishing
Buch: Automatically Ordering Events and Times in Text
Print ISBN: 978-3-319-47240-9

Electronic ISBN: 978-3-319-47241-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-47241-6_3

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