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English Resource Semantics

What it is about

This is a tutorial abstract for the English Resource Grammar (ERG), a broad-coverage HPSG grammar of English, and for its semantic output, English Resource Semantics (ERS), in Minimal Recursion Semantics. ERS carries semantic roles, the scope of quantifiers and negation, and representations of time, dates, conditionals and comparatives. Gold ERS comes from the Redwoods treebank and from DeepBank over the Wall Street Journal. The abstract lists uses of ERS in negation scope detection, textual entailment and robot commands. It contains no new measurements; it is a pointer to a precise grammar as a semantic reference for English.

Ideas

For Mova

The relevant chapters are 27-negation (the scope test in I1), 25-relative-clauses (the antecedent test in I2) and 23-complementation (controlled subjects). The code is in the prag and world crates, which read UD trees into frames and events. Measure first: negation scope of prag on a small test-only sample, because it is the cheapest check of whether the frames carry the scope information at all.

Topics: Semantic role labeling

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/n16-4001

@inproceedings{flickinger2016english,
  title = {{English Resource Semantics}},
  author = {Flickinger, Dan and Bender, Emily M. and Packard, Woodley},
  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts},
  year = {2016},
  doi = {10.18653/v1/n16-4001},
  url = {https://doi.org/10.18653/v1/n16-4001}
}

References

20 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Bond, F., Oepen, S., Nichols, E., Flickinger, D., Velldal, E., & Haugereid, P. (2011). Deep opensourc emachine translation. Machine Translation , 25 , 87105. doi: 10.1007/s1059001190994 ↗
  2. Copestake, A., Flickinger, D., Pollard, C., & Sag, I. A. (2005). Minimal Recursion Semantics. An introduction. Research on Language and Computation, 3(4), 281332. find
  3. Flickinger, D. (2000). On building a more efficient grammar by exploiting types. Natural Language Engineering, 6 (1), 1528. find our card
  4. Flickinger, D. (2011). Accuracy vs. robustness in grammar engineering. In E. M. Bender & J. E. Arnold (Eds.), Language from a cognitive perspective: Grammar, usage, and processing (pp. 3150). Stanford: CSLI Publications. find
  5. Flickinger, D., Bender, E. M., & Oepen, S. (2014). Towards an encyclopedia of compositional semantics: Documenting the interface of the english resource grammar. In N. Calzolari et al. (Eds.), Proceedings of the ninth international conference on language resources and evaluation (LREC'14) (pp. 875881). Reykjavik, Iceland: European Language Resources Association (ELRA). find
  6. Flickinger, D., Zhang, Y., & Kordoni, V. (2012). DeepBank. A dynamically annotated treebank of the Wall Street Journal. In (p. 8596). Lisbon, Portugal: Edições Colibri. find
  7. Herbelot, A., & Copestake, A. (2006). Acquiring Ontological Relationships from Wikipedia Using RMRS. In Proceedings of the ISWC 2006 workshop on web content. find
  8. Ivanova, A., Oepen, S., Dridan, R., Flickinger, D., & Øvrelid, L. (2013). On different approaches to syntactic analysis into bilexical dependencies. An empirical comparison of direct, PCFGbased, and HPSGbased parsers. In Proceedings of the 13th International Conference on Parsing Technologies (p. 6372). Nara, Japan. find
  9. Kramer, J., & Gordon, C. (2014). Improvement of a naive bayes sentiment classifier using mrsbased features. In Proceedings of the third joint conference on lexical and computational semantics (*SEM 2014) (pp. 2229). Dublin, Ireland: Association for Computational Linguistics and Dublin City University. find
  10. Lien, E., & Kouylekov, M. (2015). Semantic parsing for textual entailment. In Proceedings of the 14th International Conference on Parsing Technologies (p. 4049). Bilbao, Spain. find
  11. MacKinlay, A., Martinez, D., & Baldwin, T. (2011). A parserbased approach to detecting modification of biomedical events. In Proceedings of the acm fifth international workshop on data and text mining in biomedical informatics (pp. 5158). New York, NY, USA: ACM. find
  12. Miyao, Y., Oepen, S., & Zeman, D. (2014). InHouse. An ensemble of preexisting offtheshelf parsers. In Proceedings of the 8th International Workshop on Semantic Evaluation (p. 6372). Dublin, Ireland. find
  13. Oepen, S., Flickinger, D., Toutanova, K., & Manning, C. D. (2004). LinGO Redwoods. A rich and dynamic treebank for HPSG. Research on Language and Computation, 2(4), 575596. find our card
  14. Oepen, S., Velldal, E., Lnning, J. T., Meurer, P., Rosn, V., & Flickinger, D. (2007). Towards hybrid qualityoriented machine translation: On linguistics and probabilities in MT. In Proceedings of 11th conference on theoretical and methodological issues in machine translation (p. 144153). Skvde, Sweden. find
  15. Packard, W. (2014). UWMRS: Leveraging a deep grammar for robotic spatial commands. In Proceedings of the 8th international workshop on semantic evaluation (semeval 2014) (pp. 812816). Dublin, Ireland: Association for Computational Linguistics and Dublin City University. find
  16. Packard, W., Bender, E. M., Read, J., Oepen, S., & Dridan, R. (2014). Simple negation scope resolution through deep parsing: A semantic solution to a semantic problem. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers) (pp. 6978). Baltimore, Maryland: Association for Computational Linguistics. find
  17. Reiplinger, M., Schäfer, U., & Wolska, M. (2012). Extracting glossary sentences from scholarly articles: A comparative evaluation of pattern bootstrapping and deep analysis. In Proceedings of the ACL2012 special workshop on rediscovering 50 years of discoveries (pp. 5565). Jeju Island, Korea. find
  18. Schäfer, U., Kiefer, B., Spurk, C., Steffen, J., & Wang, R. (2011). The ACL Anthology Searchbench. find
  19. In Proceedings of the ACLHLT 2011 system demonstrations (pp. 713). Portland, Oregon: Association for Computational Linguistics. find
  20. Yao, X., Bouma, G., & Zhang, Y. (2012). Semanticsbased question generation and implementation. Dialogue & Discourse, 3(2), 1142. find

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