Skip to main content
SpringerLink
Log in

Inducing a Semantically Rich Nested Event Model

  • Conference paper
  • First Online:
Intelligent Software Methodologies, Tools and Techniques (SoMeT 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 513))

  • 832 Accesses

Abstract

Research has revealed that getting data with named entities (NEs) labels are laboured intensive and costly. This paper is proposing two approaches to enable NE classes to be added to the semantic role label (SRL) predicate-argument structure of Nested Event Model. The first approach associates SRL to Named Entity Recognition (NER), which is named as SRL-NER, to tag the appropriate entity class to the simple argument of the model. The second approach associates SRL to NER by fine-tuning entities in complex argument structures with Automatic Content Extraction (ACE) structure. This approach is called SRL-ACE-NER. Stanford NER tool is used as the benchmark for evaluation. The result shows that the proposed approaches are able to recognize more PERSON entities. However, the approaches are not able to recognize LOCATION/PLACE as efficiently as the benchmark. It is also observed that the benchmark tool is sometimes not able to tag as comprehensively as the proposed approaches. This paper has successfully demonstrated the potential of using a semantically enriched Nested Event Model as an alternative for NER technique. SRL-ACE-NER has achieved an average precision of 92 % in recognising PERSON, LOCATION/PLACE, TIME, and ORGANIZATION.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Grishman, R., Sundheim, B.: Message understanding conference - 6: a brief history. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING), pp. 466–471 (1996)

    Google Scholar 

  2. Sarawagi, S.: Information extraction. Found. Trends Databases. 1(3), 261–377 (2007)

    Article  Google Scholar 

  3. TAC KBP Track: TAC 2009 Knowledge Base Population Track. http://apl.jhu.edu/~paulmac/kbp.html

  4. Zhang, Z.: Named entity recognition-challenges in document annotation, Gazetteer construction and disambiguation. Doctor of Philosophy, The University of Sheffield (2013)

    Google Scholar 

  5. Chinchor, N.: Overview of MUC-7/MET-2. In: Proceedings of the 7th Message Understanding Conference. (1998)

    Google Scholar 

  6. Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: The automatic content extraction (ACE) program – tasks, data, and evaluation. In: Proceedings of the Conference on Language Resources and Evaluation (LREC), pp. 837–840 (2004)

    Google Scholar 

  7. Nadeau, D.: A survey on named entity recognition and classification. Linguisticae Investigationes. 30(1), 1–26 (2007)

    Article  Google Scholar 

  8. Roberts, A., Gaizasukas, R., Hepple, M., Guo, Y.: Combining terminology resources and statistical methods for entity recognition. In: Proceedings of the 6th Language Resources and Evaluation Conference (LREC), pp. 2974–2980 (2008)

    Google Scholar 

  9. Ponomareva, N., Pla, F., Molina, A., Rosso, P.: Biomedical named entity recognition: a poor knowledge HMM-based approach. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 382–387. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Kazama, J., Torisawa, K.: Exploiting wikipedia as external knowledge for named entity recognition. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 698–707 (2007)

    Google Scholar 

  11. Arnold, A., Nallapati, R., Cohen, W.: Exploiting feature hierarchy for transfer learning in named entity. In: Proceedings of ACL-08: HLT 245–253 (2008)

    Google Scholar 

  12. Finkel, J., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: 43rd Annual Meeting on ACL, pp. 363–370 (2005)

    Google Scholar 

  13. Ratinov, L., Roth, R.: Design challenges and misconceptions in named entity recognition. In: 13th Conference on Computational Natural Language Learning, pp. 147–155 (2009)

    Google Scholar 

  14. Thomson Reuters. Calais Web Service. http://www.opencalais.com/

  15. Alias-i. LingPipe 4.1.0. http://alias-i.com/lingpipe

  16. Atdag, S., Labatut, V.: A comparison of named entity recognition tools applied to biographical texts. In: 2nd International Conference on Systems and Computer Science, pp. 228–233 (2013)

    Google Scholar 

  17. Exner, P., Nugues, P.: 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 (DeRiVE 2011) (2011)

    Google Scholar 

  18. Hung, S., Lin, C., Hong, J.: Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling. Expert Syst. Appl. 37(1), 341–347 (2010)

    Article  Google Scholar 

  19. Surdeanu, M., Harabagiu, S., Williams, J., Aarseth, P.: Using predicate argument structures for information extraction. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, ACL 2003, pp. 8–15. Association for Computational Linguistics, Morristown, NJ, USA (2003)

    Google Scholar 

  20. Johansson, R.: Dependency-based semantic analysis of natural-language text. Doctor of Philosophy, thesis, Lund University (2008)

    Google Scholar 

  21. Siaw, N.H., Narayanan, K., Bali, R.-M., Jane, L.: Nested event model. In: SoMET: The 13th International Conference on Intelligent Software Methodologies Tools and Techniques (2014)

    Google Scholar 

  22. Hiong, S.N., Ranaivo-Malançon, B., Kulathuramaiyer, N., Labadin, J.: Linguistically enhanced collocate words model. In: Jaafar, A., Mohamad Ali, N., Mohd Noah, S.A., Smeaton, A.F., Bruza, P., Bakar, Z.A., Jamil, N., Sembok, T.M.T. (eds.) AIRS 2014. LNCS, vol. 8870, pp. 230–243. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  23. Wang, W., Zhao, D.: Ontology-based event modeling for semantic understanding of Chinese news story. In: Zhou, M., Zhou, G., Zhao, D., Liu, Q., Zou, L. (eds.) NLPCC 2012. CCIS, vol. 333, pp. 58–68. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  24. Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL2004), pp. 423–429 (2004)

    Google Scholar 

  25. Ding, Y., Palmer, M.: Synchronous dependency insertion grammars: a grammar formalism for syntax based statistical MT. In: Proceedings of the Workshop on Recent Advances in Dependency Grammar, pp. 90–97 (2004)

    Google Scholar 

  26. Quirk, C., Menezes, A., Cherry, C.: Dependency treelet translation: syntactically informed phrasal SMT. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL2005), pp. 271–279 (2005)

    Google Scholar 

  27. Johansson, R., Nugues, P.: Dependency-based semantic role labeling of PropBank. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 69–78 (2008)

    Google Scholar 

  28. Nivre, J.: Dependency parsing. Lang. Linguist. Compass 4(3), 138–152 (2010)

    Article  Google Scholar 

  29. Hacioglu, K:. Semantic role labeling using dependency trees. In: Proceedings of COLING (2004)

    Google Scholar 

  30. Zhao, H., Zhang, X., Kit, C.: Integrative semantic dependency parsing via efficient large-scale feature selection. J. Artif. Intell. Res. 46, 203–233 (2013)

    MathSciNet  Google Scholar 

  31. Fillmore, C.J.: Frame semantics and the nature of language. In: Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, pp. 20–32 (1976)

    Google Scholar 

  32. Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31, 71–105 (2005)

    Article  Google Scholar 

  33. Kipper-Schuler, K.: Verbnet: a broad-coverage, comprehensive verb Lexicon. Doctor of Philosophy, thesis, University of Pennsylvania (2005)

    Google Scholar 

  34. Meyers, A., Reeves, R., Macleod, C., Szekely, R., Zielinska, V., Young, B. et al.: The NomBank Project: An interim report. In: Meyers, A. (ed.) HLT-NAACL 2004 Workshop: Frontiers in Corpus Annotation, pp. 24–31 (2004)

    Google Scholar 

  35. Buchholz, S., Marsi, E., Dubey, A., Krymolowski, Y.: CoNLL-X shared task on multilingual dependency parsing. In: Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-2006) (2006)

    Google Scholar 

  36. Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryigit, G., Kübler, S., et al.: MaltParser: a language-independent system for data-driven dependency parsing. Nat. Lang. Eng. 13(2), 95–135 (2007)

    Google Scholar 

  37. Surdeanu, M., Johansson, R., Meyers, A., Màrquez, L., Nivre, J.: The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. In: Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL-2008), pp. 159–177 (2008)

    Google Scholar 

  38. Hajic, J., Ciaramita., M., Johansson, R., Kawahara, D., Marti, M.A., M`arquez, L. et al.: The CoNLL 2009 shared task: syntactic and semantic dependencies in multiple languages. In: Proceedings of the 13th CoNLL-2009, pp. 1–18 (2009)

    Google Scholar 

  39. McDonald, R., Crammer, K., Pereira, F.: Online large-margin training of dependency parsers. In: Proceedings of ACL-2005 (2005)

    Google Scholar 

  40. Johansson, R., Nugues, P.: The effect of syntactic representation on semantic role labeling. In: Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), pp. 393–400 (2008)

    Google Scholar 

  41. 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 7th International Workshop on Semantic Evaluation (SemEval 2013), pp. 1–9 (2013)

    Google Scholar 

  42. Llorens, H., Saquete, E., Navarro, B.: TipSem (English and Spanish): evaluating CRFS and semantic roles in Tempeval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 284–291 (2010)

    Google Scholar 

Download references

Acknowledegement

The sponsorship of this research is by the Education Sponsorship Division, Ministry of Education Malaysia.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University Malaysia Sarawak, Kuching, Malaysia

    Nyuk Hiong Siaw, Bali Ranaivo-Malançon, Narayanan Kulathuramaiyer & Jane Labadin

Authors
  1. Nyuk Hiong Siaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bali Ranaivo-Malançon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Narayanan Kulathuramaiyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jane Labadin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nyuk Hiong Siaw .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Johor Baharu, Malaysia

    Ali Selamat

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Siaw, N.H., Ranaivo-Malançon, B., Kulathuramaiyer, N., Labadin, J. (2015). Inducing a Semantically Rich Nested Event Model. In: Fujita, H., Selamat, A. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2014. Communications in Computer and Information Science, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-17530-0_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17530-0_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17529-4

  • Online ISBN: 978-3-319-17530-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation