ERG User Documentation
The English Resource Grammar (ERG) is a general-purpose computational grammar that, in combination with specialized processing tools, can map running English text to highly normalized logical-form representations of meaning.
An on-line demo of the ERG is available here.
Following are pointers to existing documentation on how to use the ERG, make sense of the syntactic and semantic analyses it provides, and interface to it for parsing or generation:
Linguistic Type Database, with example sentences in Redwoods Treebank
The Redwoods Treebank Many sentences parsed with the ERG.
- The latest ERG grammar files can be obtained via SVN:
$ svn checkout http://svn.delph-in.net/erg/trunk
With high parsing accuracy with rich semantic representations, English Resource Semantics is a valuable source of information for many semantically-sensitive NLP tasks. ERS-based systems have achieved state-of-the-art results in various tasks, including the identification of speculative or negated event mentions in biomedical text (MacKinlay et al 2011), question generation (Yao et al 2012), detecting the scope of negation (Packard et al 2014), relating natural language to robot control language (Packard 2014), and recognizing textual entailment (PETE task; Lien & Kouylekov 2015). ERS representations have also been beneficial in semantic transfer-based MT (Oepen et al 2007, Bond et al 2011), ontology acquisition (Herbelot & Copestake 2006), extraction of glossary sentences (Reiplinger et al 2012), sentiment analysis (Kramer & Gordon 2014), and the ACL Anthology Searchbench (Schäfer et al 2011).
Bond, F., Oepen, S., Nichols, E., Flickinger, D., Velldal, E., & Haugereid, P. (2011). Deep open-sourc emachine translation. Machine Translation , 25 , 87-105. doi: 10.1007/s10590-011-9099-4
Copestake, A., Flickinger, D., Pollard, C., & Sag, I. A. (2005). Minimal Recursion Semantics. An introduction. Research on Language and Computation, 3(4), 281-332.
Flickinger, D. (2000). On building a more efficient grammar by exploiting types. Natural Language Engineering, 6 (1), 15-28.
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. 31-50). Stanford: CSLI Publications.
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. 875-881). Reykjavik, Iceland: European Language Resources Association (ELRA).
Flickinger, D., Zhang, Y., & Kordoni, V. (2012). DeepBank. A dynamically annotated treebank of the Wall Street Journal. In (p. 85-96). Lisbon, Portugal: Edições Colibri.
Herbelot, A., & Copestake, A. (2006). Acquiring Ontological Relationships from Wikipedia Using RMRS. In Proceedings of the ISWC 2006 workshop on web content.
Kramer, J., & Gordon, C. (2014). Improvement of a naive bayes sentiment classifier using mrs-based features. In Proceedings of the third joint conference on lexical and computational semantics (*SEM 2014) (pp. 22-29). Dublin, Ireland: Association for Computational Linguistics and Dublin City University.
Lien, E., & Kouylekov, M. (2015). Semantic parsing for textual entailment. In Proceedings of the 14th International Conference on Parsing Technologies (p. 40-49). Bilbao, Spain.
MacKinlay, A., Martinez, D., & Baldwin, T. (2011). A parser-based approach to detecting modification of biomedical events. In Proceedings of the acm fifth international workshop on data and text mining in biomedical informatics (pp. 51-58). New York, NY, USA: ACM.
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), 575-596.
Oepen, S., Velldal, E., Lnning, J. T., Meurer, P., Rosn, V., & Flickinger, D. (2007). Towards hybrid quality-oriented machine translation: On linguistics and probabilities in MT. In Proceedings of 11th conference on theoretical and methodological issues in machine translation (p. 144-153). Skvde, Sweden.
Packard, W. (2014). UW-MRS: Leveraging a deep grammar for robotic spatial commands. In Proceedings of the 8th international workshop on semantic evaluation (semeval 2014) (pp. 812-816). Dublin, Ireland: Association for Computational Linguistics and Dublin City University.
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. 69-78). Baltimore, Maryland: Association for Computational Linguistics.
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 ACL-2012 special workshop on rediscovering 50 years of discoveries (pp. 55-65). Jeju Island, Korea.
Schäfer, U., Kiefer, B., Spurk, C., Steffen, J., & Wang, R. (2011). The ACL Anthology Searchbench. In Proceedings of the ACL-HLT 2011 system demonstrations (pp. 7-13). Portland, Oregon: Association for Computational Linguistics.
Yao, X., Bouma, G., & Zhang, Y. (2012). Semantics-based question generation and implementation. Dialogue & Discourse, 3(2), 11-42.