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Utilizing structured knowledge bases in open IE based event template extraction

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Abstract

Automatic template extraction including event template has been studied intensively in recent years. Researchers study the topic in order to solve the problem of manually defining a template that is required in most information extraction systems. Several studies of event template extraction rely on the documents characteristics to discover the pattern. Although there exist some structured knowledge bases, such as: FrameNet, Predicate Matrix, ACE (Automatic Content Extraction) event type keywords seeds, and FrameNet-ACE event type mapping, no previous researchers have studied combining this information for event template extraction. This paper presents an event template extraction approach that incorporates structured knowledge bases. We propose event template extraction from Open Information Extraction (Open IE) results (relation tuples) in two stages: relation tuple clustering and relation tuple filtering. Both processes utilize structured knowledge bases, as constraint sources in the clustering process and as the basis for the filtering process. The filtering process employs the word embedding representation to capture the semantic relatedness between words. We argue that by involving structured knowledge bases, the relation tuple semantic information can be enriched. Therefore, we can get groups of relation tuples with a similar event sense that represent event templates. The empirical experiment was based on an event argument extraction task and showed that our proposed approach outperforms similar methods that do not use structured knowledge bases. We also compare our proposed system performance to the performance of state-of-the-art systems. The comparison result shows that our proposed system outperforms other state-of-the-art systems, in terms of precision.

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Notes

  1. https://github.com/U-Alberta/exemplar

  2. We made the modification on the seed list by placing the word fire that originally listed on End-Position event type to Attack event type. We think it is more suitable with the document domain that we use in experiment.

  3. The dataset used in experiment could be accessed in https://github.com/aromadhony/kb-openie-eventtemplate

References

  1. Altmeyer R, Grishman R (2009) Active learning of event detection patterns. Proteus Project Technical Report, pp 09–014

  2. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, society for industrial and applied mathematics, pp 1027–1035

  3. Baker CF, Fillmore CJ, Lowe JB (1998) The berkeley framenet project. In: Proceedings of the 17th international conference on computational linguistics, vol 1. Association for Computational Linguistics, pp 86–90

  4. Balasubramanian N, Soderland S, Mausam OE, Etzioni O (2013) Generating coherent event schemas at scale. In: EMNLP, pp 1721–1731

  5. Banko M, Cafarella MJ, Soderland S, Broadhead M, Etzioni O (2007) Open information extraction from the web. In: IJCAI, vol 7, pp 2670–2676

  6. Bronstein O, Dagan I, Li Q, Ji H, Frank A (2015) Seed-based event trigger labeling: how far can event descriptions get us?. In: ACL, vol 2, pp 372–376

  7. Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5. Atlanta, p 3

  8. Chambers N (2013) Event schema induction with a probabilistic entity-driven model. In: EMNLP, vol 13, pp 1797–1807

  9. Chambers N, Jurafsky D (2011) Template-based information extraction without the templates. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, pp 976–986

  10. De Lacalle ML, Laparra E, Rigau G (2014) Predicate matrix: extending semlink through wordnet mappings. In: LREC, pp 903–909

  11. Doddington GR, Mitchell A, Przybocki MA, Ramshaw LA, Strassel S, Weischedel RM (2004) The automatic content extraction (ace) program-tasks, data, and evaluation. In: LREC, vol 2, pp 837–840

  12. Etzioni O, Fader A, Christensen J, Soderland S, Mausam M (2011) Open information extraction: the second generation. In: IJCAI, vol 11, pp 3–10

  13. Exner P, Nugues P (2011) Using semantic role labeling to extract events from wikipedia. In: Proceedings of the workshop on detection, representation, and exploitation of events in the semantic web (DeRiVE 2011). Workshop in conjunction with the 10th international semantic web conference, pp 23–24

  14. Fader A, Zettlemoyer L, Etzioni O (2014) Open question answering over curated and extracted knowledge bases. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1156–1165

  15. Feng X, Huang L, Tang D, Ji H, Qin B, Liu T (2016) A language-independent neural network for event detection. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers), vol 2, pp 66–71

  16. Goikoetxea J, Agirre E, Soroa A (2016) Single or multiple? Combining word representations independently learned from text and wordnet. In: AAAI, pp 2608–2614

  17. Grycner A, Weikum G (2014) Harpy: hypernyms and alignment of relational paraphrases. In: 25th International conference on computational linguistics. ACL, pp 2195–2204

  18. Huang L, Cassidy T, Feng X, Ji H, Voss CR, Han J, Sil A (2016) Liberal event extraction and event schema induction. In: ACL (1)

  19. Izquierdo R, Suárez A, Rigau G (2007) Exploring the automatic selection of basic level concepts. In: Proceedings of RANLP, vol 7

  20. Jiang T, Sha L, Sui Z (2014) Event schema induction based on relational co-occurrence over multiple documents. In: Natural language processing and chinese computing. Springer, pp 23–33

  21. Kokkinakis D (2012) Initial experiments of medication event extraction using frame semantics. In: Scandinavian Conference on Health Informatics 2012; October 2-3; Linköping; Sverige, Linköping University Electronic Press, vol 070, pp 41–47

  22. (LDC) LDC (2005) English annotation guidelines for entities. https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v5.6.6.pdf, visited 20-July-2018

  23. (LDC) LDC (2005) English annotation guidelines for events. https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-events-guidelines-v5.4.3.pdf, visited 20-July-2018

  24. Li Q, Ji H, Hong Y, Li S (2014) Constructing information networks using one single model. In: EMNLP, pp 1846–1851

  25. Li XL, Liu B, Ng SK (2010) Negative training data can be harmful to text classification. In: Proceedings of the 2010 conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 218–228

  26. Liu S, Chen Y, He S, Liu K, Zhao J (2016) Leveraging framenet to improve automatic event detection. In: ACL (1)

  27. Liu S, Chen Y, Liu K, Zhao J (2017) Exploiting argument information to improve event detection via supervised attention mechanisms. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), vol 1, pp 1789–1798

  28. Mesquita F, Schmidek J, Barbosa D (2013) Effectiveness and efficiency of open relation extraction. New York Times 500:150

    Google Scholar 

  29. Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41

    Article  Google Scholar 

  30. Mitchell J, Lapata M (2008) Vector-based models of semantic composition. In: Proceedings of ACL-08: HLT pp 236–244

  31. Nakashole N, Weikum G, Suchanek F (2012) Discovering and exploring relations on the web. Proc VLDB Endowment 5(12):1982–1985

    Article  Google Scholar 

  32. Nguyen KH, Tannier X, Ferret O, Besanċon R (2015) Generative event schema induction with entity disambiguation. In: ACL, vol 1, pp 188–197

  33. Nguyen KH, Tannier X, Ferret O, Besanċon R (2016) A dataset for open event extraction in english. In: LREC

  34. Nguyen TH, Grishman R (2018) Graph convolutional networks with argument-aware pooling for event detection. In: The Thirty-Second AAAI conference on artificial intelligence (AAAI-18)

  35. Niraula NB, Gautam D, Banjade R, Maharjan N, Rus V (2015) Combining word representations for measuring word relatedness and similarity. In: FLAIRS Conference, pp 199–204

  36. Peng H, Song Y, Roth D (2016) Event detection and co-reference with minimal supervision. In: EMNLP, pp 392–402

  37. Qin B, Zhao Y, Ding X, Liu T, Zhai G (2010) Event type recognition based on trigger expansion. Tsinghua Sci Technol 15(3):251–258

    Article  Google Scholar 

  38. Ruppenhofer J, Ellsworth M, Petruck MR, Johnson CR, Scheffczyk J (2006) Framenet ii: extended theory and practice

  39. Schmitz M, Bart R, Soderland S, Etzioni O, et al. (2012) Open language learning for information extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 523– 534

  40. Segers R, Laparra E, Rospocher M, Vossen P, Rigau G, Ilievski F (2016) The pred-icate matrix and the event and implied situation ontology: making more of events. In: Proceedings of GWC2016

  41. Sha L, Li S, Chang B, Sui Z (2016) Joint learning templates and slots for event schema induction. In: Proceedings of NAACL-HLT, pp 428–434

  42. Socher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 1201–1211

  43. Stanovsky G, Dagan I, et al. (2015) Open ie as an intermediate structure for semantic tasks. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 2: short papers), vol 2, pp 303–308

  44. Wagstaff K, Cardie C, Rogers S, Schrödl S, et al. (2001) Constrained k-means clustering with background knowledge. In: ICML, vol 1, pp 577–584

  45. Widyantoro DH (2004) Concept drift learning and its application to adaptive information filtering. PhD thesis, Texas A&M University

  46. Yates AP, Etzioni O (2009) Unsupervised methods for determining object and relation synonyms on the web. Journal of Artificial Intelligence Research

  47. Zhou W, Zhang Y, Su X, Li Y, Liu Z (2016) Semantic role labeling based event argument identification. Int J Database Theory Appl 9(6):93–102

    Article  Google Scholar 

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Acknowledgements

This work was funded by Institut Teknologi Bandung, under the P3MI program.

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  1. School of Electrical Engineering and Informatics Institut Teknologi Bandung, Jl. Ganesha no. 10, Bandung, Indonesia

    Ade Romadhony, Dwi H. Widyantoro & Ayu Purwarianti

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  1. Ade Romadhony
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  2. Dwi H. Widyantoro
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  3. Ayu Purwarianti
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Correspondence to Ade Romadhony.

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Romadhony, A., Widyantoro, D.H. & Purwarianti, A. Utilizing structured knowledge bases in open IE based event template extraction. Appl Intell 49, 206–219 (2019). https://doi.org/10.1007/s10489-018-1269-0

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