Language Resources and Evaluation

, Volume 51, Issue 1, pp 37–66 | Cite as

A multilingual FrameNet-based grammar and lexicon for controlled natural language

  • Normunds GruzitisEmail author
  • Dana Dannélls
Original Paper


Berkeley FrameNet is a lexico-semantic resource for English based on the theory of frame semantics. It has been exploited in a range of natural language processing applications and has inspired the development of framenets for many languages. We present a methodological approach to the extraction and generation of a computational multilingual FrameNet-based grammar and lexicon. The approach leverages FrameNet-annotated corpora to automatically extract a set of cross-lingual semantico-syntactic valence patterns. Based on data from Berkeley FrameNet and Swedish FrameNet, the proposed approach has been implemented in Grammatical Framework (GF), a categorial grammar formalism specialized for multilingual grammars. The implementation of the grammar and lexicon is supported by the design of FrameNet, providing a frame semantic abstraction layer, an interlingual semantic application programming interface (API), over the interlingual syntactic API already provided by GF Resource Grammar Library. The evaluation of the acquired grammar and lexicon shows the feasibility of the approach. Additionally, we illustrate how the FrameNet-based grammar and lexicon are exploited in two distinct multilingual controlled natural language applications. The produced resources are available under an open source license.


FrameNet Grammatical Framework Multilinguality  Natural language generation Controlled natural language 



This work has been supported by the Swedish Research Council under Grant No. 2012-5746 (Reliable Multilingual Digital Communication: Methods and Applications) and by the Centre for Language Technology in Gothenburg. The research leading to these results has received funding also from the Latvian State Research Programme NexIT (Project No. 1).


  1. Barzdins, G. (2014). FrameNet CNL: A knowledge representation and information extraction language. In Controlled natural language, Lecture Notes in Computer Science (Vol. 8625, 90–101). Berlin: Springer.Google Scholar
  2. Boas, H. C. (2009). Multilingual FrameNets in computational lexicography: Methods and applications. Berlin: Mouton de Gruyter.CrossRefGoogle Scholar
  3. Borin, L., Dannélls, D., Forsberg, M., Toporowska Gronostaj, M., & Kokkinakis, D. (2010). The past meets the present in Swedish FrameNet++. In Proceedings of the 14th EURALEX international congress (pp. 269–281).Google Scholar
  4. Borin, L., Forsberg, M., & Roxendal, J. (2012). Korp—The corpus infrastructure of Språkbanken. In Proceedings of the 8th international conference on language resources and evaluation (LREC) (pp. 474–478).Google Scholar
  5. Borin, L., Forsberg, M., & Lönngren, L. (2013). SALDO: A touch of yin to WordNet’s yang. Language Resources and Evaluation, 47(4), 1191–1211.CrossRefGoogle Scholar
  6. Coyne, B., Bauer, D., & Rambow, O. (2011). VigNet: Grounding language in graphics using frame semantics. In Proceedings of the ACL workshop on relational models of semantics (pp. 28–36).Google Scholar
  7. Dannélls, D. (2010). Discourse generation from formal specifications using the Grammatical Framework, GF. Special Issue of Research in Computing Science, 46, 167–178.Google Scholar
  8. Dannélls, D., & Gruzitis, N. (2014a). Extracting a bilingual semantic grammar from FrameNet-annotated corpora. In Proceedings of the 9th international language resources and evaluation conference (LREC) (pp. 2466–2473).Google Scholar
  9. Dannélls, D., & Gruzitis, N. (2014b). Controlled natural language generation from a multilingual FrameNet-based grammar. In Controlled natural language, Lecture Notes in Computer Science (Vol. 8625, pp. 155–166). Berlin: Springer.Google Scholar
  10. Dannélls, D., Enache, R., Damova, M., & Chechev, M. (2012). Multilingual online generation from Semantic Web ontologies. In Proceedings of the 21st international world wide web conference, European Project Track (pp. 239–242).Google Scholar
  11. Das, D., Chen, D., Martins, A. F. T., Schneider, N., & Smith, N. A. (2014). Frame-semantic parsing. Computational Linguistics, 40(1), 9–56.CrossRefGoogle Scholar
  12. Davis, B., Enache, R., van Grondelle, J., & Pretorius, L. (2012). Multilingual verbalisation of modular ontologies using GF and LEMON. In Controlled natural language, Lecture Notes in Computer Science (Vol. 7427, pp. 167–184). Berlin: SpringerGoogle Scholar
  13. de Marneffe M.C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., & Manning, C. D. (2014). Universal stanford dependencies: A cross-linguistic typology. In Proceedings of the 9th international language resources and evaluation conference (LREC) (pp. 4585–4592).Google Scholar
  14. Ferrández, Ó., Ellsworth, M., Muñoz, R., & Baker, C. F. (2010). Aligning FrameNet and WordNet based on semantic neighborhoods. In Proceedings of the 7th international language resources and evaluation conference (LREC) (pp. 310–314).Google Scholar
  15. Fillmore, C. J. (1985). Frames and the semantics of understanding. Quaderni di Semantica, 6(2), 222–254.Google Scholar
  16. Fillmore, C. J., Johnson, C. R., & Petruck, M. R. L. (2003). Background to framenet. International Journal of Lexicography, 16(3), 235–250.CrossRefGoogle Scholar
  17. Gruzitis, N., & Barzdins, G. (2010). Polysemy in controlled natural language texts. In Controlled natural language, Lecture Notes in Computer Science (Vol. 5972, pp. 102–120). Berlin: Springer.Google Scholar
  18. Gruzitis, N., Paikens, P., & Barzdins, G. (2012). FrameNet resource grammar library for GF. In Controlled natural language, Lecture Notes in Computer Science (Vol. 7427, pp. 121–137). Berlin: Springer.Google Scholar
  19. Kuhn, T. (2014). A survey and classification of controlled natural languages. Computational Linguistics, 40(1), 121–170.CrossRefGoogle Scholar
  20. Lenci, A., Bel, N., Busa, F., Calzolari, N., Gola, E., Monachini, M., et al. (2000). SIMPLE: A general framework for the development of multilingual lexicons. International Journal of Lexicography, 13(4), 249–263.CrossRefGoogle Scholar
  21. Meyers, A., Macleod, C., & Grishman, R. (1995). COMLEX Syntax 2.0 manual for tagged entries. Technical Report, New York University.Google Scholar
  22. Moschitti, A., Morarescu, P., & Harabagiu, S. M. (2003). Open domain information extraction via automatic semantic labeling. In Proceedings of the 16th international FLAIRS conference (pp. 397–401).Google Scholar
  23. Nivre, J., Hall, J., & Nilsson, J. (2004). Memory-based dependency parsing. In Proceedings of the 8th conference on computational natural language learning (CoNLL) (pp. 49–56).Google Scholar
  24. Ranta, A. (2004). Grammatical Framework, a type-theoretical grammar formalism. Journal of Functional Programming, 14(2), 145–189.CrossRefGoogle Scholar
  25. Ranta, A. (2009). The GF resource grammar library. Linguistic Issues in Language Technology (LILT), 2(2), 1–63.Google Scholar
  26. Ranta, A., Enache, R., & Détrez, G. (2010). Controlled language for everyday use: The MOLTO Phrasebook. In Controlled natural language, Lecture Notes in Computer Science (Vol. 7175, pp. 115–136). Berlin: Springer.Google Scholar
  27. Roth, M., & Frank, A. (2009). A NLG-based application for walking directions. In Proceedings of the 47th ACL and the 4th IJCNLP conference (pp. 37–40).Google Scholar
  28. Roth, M., & Frank, A. (2010). Computing EM-based alignments of routes and route directions as a basis for natural language generation. In Proceedings of the 23rd international conference on computational linguistics (COLING) (pp. 958–966).Google Scholar
  29. Ruppenhofer, J., Ellsworth, M., Petruck, M. R. L., Johnson, C. R., & Scheffczyk, J. (2010). FrameNet II: Extended theory and practice. Berkeley: International Computer Science Institute.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of GothenburgGöteborgSweden
  2. 2.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia
  3. 3.Department of SwedishUniversity of GothenburgGöteborgSweden

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