Functional Programming Resources
- Lambda Notebook
This project is an IPython framework for linguists and especially semanticists developing analyses in compositional semantics. It aims to provide a means of developing ‘digital fragments’, following from the method of fragments in Montague grammar.
- Lambda Evaluator
Online program that allows you to write lambda terms and evaluate (that is, normalize) them, and inspect the results.
General Resources for Computational Semantics
- J. Pustejovsky, A. Stubbs 2012. Natural Language Annotation for Machine Learning, O’Reilly Publishers.
- J. Pustejovsky, A. Rumshisky, O. Batiukova, J. Moszkowicz. Annotation of Compositional Operations with GLML. In H. Bunt, J. Bos and S. Pulman (eds.), Computing Meaning, Volume 4. Springer-Verlag, 2014, pp. 217-234.
- J. Pustejovsky, A. Rumshisky, A. Plotnick, E. Jezek, O. Batiukova, V. Quochi. SemEval-2010 Task 7: Argument Selection and Coercion. Fifth International Workshop on Semantic Evaluation (SemEval-2010). Association for Computational Linguistics. Uppsala, Sweden.
Annotating Selectional Preferences, Semantic Role Labeling
- Fossati, Marco, Sara Tonelli, and Claudio Giuliano. “Frame Semantics Annotation Made Easy with DBpedia.”
Annotating Word Senses
- Verhagen, Marc, Robert Gaizauskas, Frank Schilder, Mark Hepple, Graham Katz, and James Pustejovsky. “Semeval-2007 task 15: Tempeval temporal relation identification.” In Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 75-80. Association for Computational Linguistics, 2007.
- Verhagen, Marc, Roser Sauri, Tommaso Caselli, and James Pustejovsky. “SemEval-2010 task 13: TempEval-2.” In Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 57-62. Association for Computational Linguistics, 2010.
- W. Sun, A. Rumshisky, Ö. Uzuner. 2013. Annotating temporal information in clinical narratives. J Biomed Inform. 2013 Jul 19.
- W. Sun, A. Rumshisky, Ö. Uzuner. 2013. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge . J Am Med Inform Assoc. 2013 Apr 5.
Task Decomposition with Amazon Mechanical Turk (AMT)
- Snow, Rion, et al. “Cheap and fast – but is it good? Evaluating non-expert annotations for natural language tasks.” Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, 2008.>
- Callison-Burch, Chris. “Fast, cheap, and creative: evaluating translation quality using Amazon’s Mechanical Turk.” Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009.
- Voyer, Robert, et al. “A hybrid model for annotating named entity training corpora.” Proceedings of the fourth linguistic annotation workshop. Association for Computational Linguistics, 2010.