2020/21
803 - Masters Centre of the Department of Translation and Language Sciences
8025 - Master in Intelligent Interactive Systems
32489 - Computational Semantics
Gemma Boleda Torrent, Matthijs Westera
Bibliography and information resources
Bibliography:
- Jurafsky, Daniel & Martin, James H. (2009), Speech and Language Processing: An Introduction to
Natural Language Processing, Computational Linguistics, and Speech Recognition. 3rd edition. Prentice
Hall. https://web.stanford.edu/~jurafsky/slp3
Recommended readings:
Kilgarriff, Adam. I don't believe in word senses. Computers and the Humanities 31.2 (1997): 91-113.
Murphy, Gregory L. (2002). The big book of concepts. Cambridge, MA: MIT Press. Note: Really great book I recommend to everybody. See especially Chapter 11.
- Symbolic (formal semantics, DRT-based) system for the processing of free English text (not covered in the course):
Bos, Johan (2008). Wide-coverage semantic analysis with Boxer. Proceedings of the 2008 Conference on Semantics in Text Processing. Association for Computational Linguistics.
Bos, J., & Markert, K. (2005). Recognising textual entailment with logical inference. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 628-635). Association for Computational Linguistics.
- Distributional semantics, general:
M. Baroni and A. Lenci. 2010. Distributional Memory: A general framework for corpus-based semantics. Computational Linguistics 36(4): 673-721.
Boleda, G. Distributional Semantics and Linguistic Theory. Annual Review of Linguistics. Accepted for publication. Note: survey article.
Katrin Erk. Vector space models of word meaning and phrase meaning: a survey. Language and Linguistics Compass 6(10), 635-653, October 2012. Note: survey article.
Stephen Clark. 2015. Vector Space Models of Lexical Meaning. Handbook of Contemporary Semantic Theory — second edition, edited by Shalom Lappin and Chris Fox. Chapter 16, pp.493-522. Wiley-Blackwell. [PDF] (pre-copy editing). Note: survey article.
Alessandro Lenci. 2008. Distributional semantics in linguistic and cognitive research. Italian journal of linguistics, 20 (1), pp. 1-31.
- Multimodal distributional semantics:
Bruni, E., G. Boleda, M. Baroni, N. K. Tran. 2012. Distributional semantics in technicolor. Proceedings of ACL 2012, pp. 136-145, Jeju Island, Korea.
Silberer, C and Lapata, M. 2013. Learning Grounded Meaning Representations with Autoencoders. Proceedings of ACL 2013.
M. Baroni. 2016. Grounding distributional semantics in the visual world. Language and Linguistics Compass 10(1): 3-13. Note: survey article.
- Composition in distributional semantics:
M. Baroni. 2013. Composition in distributional semantics. Language and Linguistics Compass 7(10): 511-522. Note: survey article.
Jeff Mitchell and Mirella Lapata. 2008. Vector-based Models of Semantic Composition. In: ACL. 2008, pp. 236–244.
E. Vecchi, M. Marelli, R. Zamparelli and M. Baroni. 2017. Spicy adjectives and nominal donkeys: Capturing semantic deviance using compositionality in distributional spaces. Cognitive Science 41(1): 102-136.
Marelli, M., & Baroni, M. (2015). Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics. Psychological Review, 122(3), 485–515. http://doi.org/10.1037/a0039267
Socher, R., Pennington, J., Huang, E.H., Ng, A.Y. and Manning, C.D. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the conference on empirical methods in natural language processing (pp. 151-161).
- Building word vectors with neural networks:
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer. Deep contextualized word representations. Proceedings of NAACL 2018.
Jeffrey Pennington, Richard Socher, Christopher Manning. 2014. Glove: Global vectors for word representation. Proceedings of EMNLP.
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781v3.
M. Baroni, G. Dinu and G. Kruszewski. 2014. Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Proceedings of ACL 2014 (52nd Annual Meeting of the Association for Computational Linguistics), East Stroudsburg PA: ACL, 238-247.
Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 746–751). Atlanta, Georgia: Association for Computational Linguistics.
- Current limits of distributional semantics / neural networks:
Bernardi, R., G. Boleda, R. Fernandez, D. Paperno. 2015. Distributional semantics in use. Proceedings of EMNLP 2015 Workshop LSDSem 2015: Linking Models of Lexical, Sentential and Discourse-level Semantics, 95-101. Lisbon, Portugal, September. Association for Computational Linguistics.
Paperno, D., G. Kruszewski, A. Lazaridou, Q. Ngoc, R. Bernardi, S. Pezzelle, M. Baroni, G. Boleda, R. Fernandez. 2016. The LAMBADA dataset: Word prediction requiring a broad discourse context. Proceedings of ACL 2016 (54th Annual Meeting of the Association for Computational Linguistics), 1525-1534, Berlin, Germany, August. Association for Computational Linguistics.
Boleda, G. and A. Herbelot. 2016. Formal Distributional Semantics: Introduction to the Special Issue. Computational Linguistics 42:4, 619-635.
- General Machine Learning:
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. http://m.mind.oxfordjournals.org/content/LIX/236/433.full.pdf, (if that fails: http://phil415.pbworks.com/f/TuringComputing.pdf)
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78. http://doi.org/10.1145/2347736.2347755
Parloff, R. (2016). The AI Revolution: Why Deep Learning Is Suddenly Changing Your Life. Fortune Magazine. Note: well written, thorough popular science article about deep learning.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. http://doi.org/10.1038/nature14539.