Skip to main content

2018 | OriginalPaper | Buchkapitel

Cross-Scenario Inference Based Event-Event Relation Detection

verfasst von : Yu Hong, Jingli Zhang, Rui Song, Jianmin Yao

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Event-Event Relation Detection (RD\(_{2e}\)) aims to detect the relations between a pair of news events, such as Causal relation between Criminal and Penal events. In general, RD\(_{2e}\) is a challenging task due to the lack of explicit linguistic feature signaling the relations. We propose a cross-scenario inference method for RD\(_{2e}\). By utilizing conceptualized scenario expression and graph-based semantic distance perception, we retrieve semantically similar historical events from Gigaword. Based on explicit relations of historical events, we infer implicit relations of target events by means of transfer learning. Experiments on 10 relation types show that our method outperforms the supervised models.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Abe, S., Inui, K., Matsumoto, Y.: Acquiring event relation knowledge by learning cooccurrence patterns and fertilizing cooccurrence samples with verbal nouns. In: IJCNLP, pp. 497–504 (2008) Abe, S., Inui, K., Matsumoto, Y.: Acquiring event relation knowledge by learning cooccurrence patterns and fertilizing cooccurrence samples with verbal nouns. In: IJCNLP, pp. 497–504 (2008)
2.
Zurück zum Zitat Abe, S., Inui, K., Matsumoto, Y.: Two-phased event relation acquisition: coupling the relation-oriented and argument-oriented approaches. In: COLING, pp. 1–8 (2008) Abe, S., Inui, K., Matsumoto, Y.: Two-phased event relation acquisition: coupling the relation-oriented and argument-oriented approaches. In: COLING, pp. 1–8 (2008)
3.
Zurück zum Zitat Blanco, E., Castell, N., Moldovan, D.I.: Causal relation extraction. In: LREC (2008) Blanco, E., Castell, N., Moldovan, D.I.: Causal relation extraction. In: LREC (2008)
4.
Zurück zum Zitat Caselli, T., Fokkens, A., Morante, R., Vossen, P.: Spinoza vu: an NLP pipeline for cross document timelines. In: SemEval-2015 p. 787 (2015) Caselli, T., Fokkens, A., Morante, R., Vossen, P.: Spinoza vu: an NLP pipeline for cross document timelines. In: SemEval-2015 p. 787 (2015)
6.
Zurück zum Zitat Do, Q.X., Chan, Y.S., Roth, D.: Minimally supervised event causality identification. In: EMNLP, pp. 294–303 (2011) Do, Q.X., Chan, Y.S., Roth, D.: Minimally supervised event causality identification. In: EMNLP, pp. 294–303 (2011)
7.
Zurück zum Zitat Gildea, D., Palmer, M.: The necessity of parsing for predicate argument recognition. In: ACL, pp. 239–246 (2002) Gildea, D., Palmer, M.: The necessity of parsing for predicate argument recognition. In: ACL, pp. 239–246 (2002)
8.
Zurück zum Zitat Girju, R., Moldovan, D.I., et al.: Text mining for causal relations. In: FLAIRS, pp. 360–364 (2002) Girju, R., Moldovan, D.I., et al.: Text mining for causal relations. In: FLAIRS, pp. 360–364 (2002)
9.
Zurück zum Zitat Hong, Y., Zhang, T., O’Gorman, T., Horowit-Hendler, S., Ji, H., Palmer, M.: Building a cross-document event-event relation corpus. In: LAW X, p. 1 (2016) Hong, Y., Zhang, T., O’Gorman, T., Horowit-Hendler, S., Ji, H., Palmer, M.: Building a cross-document event-event relation corpus. In: LAW X, p. 1 (2016)
10.
Zurück zum Zitat Inui, T., Inui, K., Matsumoto, Y.: Acquiring causal knowledge from text using the connective marker tame. TALIP 4(4), 435–474 (2005)CrossRef Inui, T., Inui, K., Matsumoto, Y.: Acquiring causal knowledge from text using the connective marker tame. TALIP 4(4), 435–474 (2005)CrossRef
12.
Zurück zum Zitat Lapata, M., Lascarides, A.: Learning sentence-internal temporal relations. J. Artif. Intell. Res. (JAIR) 27, 85–117 (2006)CrossRef Lapata, M., Lascarides, A.: Learning sentence-internal temporal relations. J. Artif. Intell. Res. (JAIR) 27, 85–117 (2006)CrossRef
13.
Zurück zum Zitat Lin, Z., Ng, H.T., Kan, M.Y.: A PDTB-styled end-to-end discourse parser. Nat. Lang. Eng. 20(02), 151–184 (2014)CrossRef Lin, Z., Ng, H.T., Kan, M.Y.: A PDTB-styled end-to-end discourse parser. Nat. Lang. Eng. 20(02), 151–184 (2014)CrossRef
14.
Zurück zum Zitat Mani, I., Verhagen, M., Wellner, B., Lee, C.M., Pustejovsky, J.: Machine learning of temporal relations. In: COLING and ACL, pp. 753–760 (2006) Mani, I., Verhagen, M., Wellner, B., Lee, C.M., Pustejovsky, J.: Machine learning of temporal relations. In: COLING and ACL, pp. 753–760 (2006)
15.
Zurück zum Zitat Migon, H.S., Gamerman, D., Louzada, F.: Statistical inference: an integrated approach. CRC Press, Boca Raton (2014)MATH Migon, H.S., Gamerman, D., Louzada, F.: Statistical inference: an integrated approach. CRC Press, Boca Raton (2014)MATH
16.
Zurück zum Zitat Minard, A.L., et al.: Semeval-2015 task 4: Timeline: Cross-document event ordering. In: SemEval, pp. 778–786 (2015) Minard, A.L., et al.: Semeval-2015 task 4: Timeline: Cross-document event ordering. In: SemEval, pp. 778–786 (2015)
17.
Zurück zum Zitat Miwa, M., Bansal, M.: End-to-end relation extraction using lstms on sequences and tree structures (2016). arXiv preprint. arXiv:1601.00770 Miwa, M., Bansal, M.: End-to-end relation extraction using lstms on sequences and tree structures (2016). arXiv preprint. arXiv:​1601.​00770
18.
Zurück zum Zitat Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: ACL and AFNLP, pp. 683–691 (2009) Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: ACL and AFNLP, pp. 683–691 (2009)
19.
Zurück zum Zitat Prasad, R., et al.: The penn discourse treebank 2.0. In: LREC (2008) Prasad, R., et al.: The penn discourse treebank 2.0. In: LREC (2008)
20.
Zurück zum Zitat Pustejovsky, J., Verhagen, M.: Semeval-2010 task 13: evaluating events, time expressions, and temporal relations (tempeval-2). In: Workshop on Semantic Evaluations, pp. 112–116 (2009) Pustejovsky, J., Verhagen, M.: Semeval-2010 task 13: evaluating events, time expressions, and temporal relations (tempeval-2). In: Workshop on Semantic Evaluations, pp. 112–116 (2009)
21.
Zurück zum Zitat Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: WWW, pp. 909–918 (2012) Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: WWW, pp. 909–918 (2012)
22.
Zurück zum Zitat Raja, K., Subramani, S., Natarajan, J.: Ppinterfinder–a mining tool for extracting causal relations on human proteins from literature. Database 2013, bas052 (2013) Raja, K., Subramani, S., Natarajan, J.: Ppinterfinder–a mining tool for extracting causal relations on human proteins from literature. Database 2013, bas052 (2013)
23.
Zurück zum Zitat Rugemalira, J.M.: What is a symmetrical language? multiple object constructions in Bantu. In: Berkeley Linguistics Society, vol. 17 (2012) Rugemalira, J.M.: What is a symmetrical language? multiple object constructions in Bantu. In: Berkeley Linguistics Society, vol. 17 (2012)
24.
Zurück zum Zitat Santos, C.N.d., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks (2015). arXiv preprint arXiv:1504.06580 Santos, C.N.d., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks (2015). arXiv preprint arXiv:​1504.​06580
25.
Zurück zum Zitat Velupillai, S., Mowery, D.L., Abdelrahman, S., Christensen, L., Chapman, W.W.: Blulab: Temporal information extraction for the 2015 clinical tempeval challenge. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 815-819 (2015) Velupillai, S., Mowery, D.L., Abdelrahman, S., Christensen, L., Chapman, W.W.: Blulab: Temporal information extraction for the 2015 clinical tempeval challenge. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 815-819 (2015)
26.
Zurück zum Zitat Zhou, Z.M., Xu, Y., Niu, Z.Y., Lan, M., Su, J., Tan, C.L.: Predicting discourse connectives for implicit discourse relation recognition. In: COLING, pp. 1507–1514 (2010) Zhou, Z.M., Xu, Y., Niu, Z.Y., Lan, M., Su, J., Tan, C.L.: Predicting discourse connectives for implicit discourse relation recognition. In: COLING, pp. 1507–1514 (2010)
27.
Zurück zum Zitat Zou, B., Zhou, G., Zhu, Q.: Tree kernel-based negation and speculation scope detection with structured syntactic parse features. In: EMNLP, pp. 968–976 (2013) Zou, B., Zhou, G., Zhu, Q.: Tree kernel-based negation and speculation scope detection with structured syntactic parse features. In: EMNLP, pp. 968–976 (2013)
Metadaten
Titel
Cross-Scenario Inference Based Event-Event Relation Detection
verfasst von
Yu Hong
Jingli Zhang
Rui Song
Jianmin Yao
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-99501-4_22

Premium Partner