Conference article

Fully Delexicalized Contexts for Syntax-Based Word Embeddings

Jenna Kanerva
TurkuNLP Group, University of Turku, Graduate School (UTUGS), Turku, Finland

Sampo Pyysalo
Language Technology Lab DTAL, University of Cambridge, United Kingdom

Filip Ginter
TurkuNLP Group, University of Turku, Finland

Download article

Published in: Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017), September 18-20, 2017, Università di Pisa, Italy

Linköping Electronic Conference Proceedings 139:11, s. 83-91

Show more +

Published: 2017-09-13

ISBN: 978-91-7685-467-9

ISSN: 1650-3686 (print), 1650-3740 (online)


Word embeddings induced from large amounts of unannotated text are a key resource for many NLP tasks. Several recent studies have proposed extensions of the basic distributional semantics approach where words form the context of other words, adding features from e.g. syntactic dependencies. In this study, we look in a different direction, exploring models that leave words out entirely, instead basing the context representation exclusively on syntactic and morphological features. Remarkably, we find that the resulting vectors still capture clear semantic aspects of words in addition to syntactic ones. We assess the properties of the vectors using both intrinsic and extrinsic evaluations, demonstrating in a multilingual parsing experiment using 55 treebanks that fully delexicalized syntax-based word representations give a higher average parsing performance than conventional word2vec embeddings.


No keywords available


Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pas¸ca, and Aitor Soroa. 2009. A study on similarity and relatedness using distributional and wordnet-based approaches. In Proceedings of HLT-NAACL’09, pages 19–27.

Chris Alberti, Daniel Andor, Ivan Bogatyy, Michael Collins, Dan Gillick, Lingpeng Kong, Terry Koo, Ji Ma, Mark Omernick, Slav Petrov, et al. 2017. Syntaxnet models for the conll 2017 shared task. arXiv preprint arXiv:1703.04929.

Simon Baker, Roi Reichart, and Anna Korhonen. 2014. An unsupervised model for instance level subcategorization acquisition. In Proceedings of EMNLP’14, pages 278–289.

Miroslav Batchkarov, Thomas Kober, Jeremy Reffin, Julie Weeds, and David Weir. 2016. A critique of word similarity as a method for evaluating distributional semantic models. In Proceedings of RepEval’ 16.

Yoshua Bengio, R´ejean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of machine learning research, 3(Feb):1137–1155.

Bernd Bohnet, Joakim Nivre, Igor Boguslavsky, Richrd arkas, Filip Ginter, and Jan Haji. 2013. Joint morphological and syntactic analysis for richly inflected languages. Transactions of the Association for Computational Linguistics, 1:415–428.

Bernd Bohnet. 2010. Very high accuracy and fast dependency parsing is not a contradiction. In Proceedings of the 23rd international conference on computational linguistics, pages 89–97.

Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012. Distributional semantics in technicolor. In Proceedings of ACL’12, pages 136–145.

Billy Chiu, Anna Korhonen, and Sampo Pyysalo. 2016. Intrinsic evaluation of word vectors fails to predict extrinsic performance. In Proceedings of RepEval’16, pages 1–6.

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493–2537.

Manaal Faruqui and Chris Dyer. 2014. Community evaluation and exchange of word vectors at wordvectors. org. In Proceedings of ACL’14.

Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, and Chris Dyer. 2016. Problems with evaluation of word embeddings using word similarity tasks. In Proceedings of RepEval’16, pages 30–35.

Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. In Proceedings of WWW’01, pages 406–414.

Daniela Gerz, Ivan Vulic, Felix Hill, Roi Reichart, and Anna Korhonen. 2016. SimVerb-3500: A largescale evaluation set of verb similarity. In Proceedings of EMNLP’16.

Filip Ginter, Jan Hajic, Juhani Luotolahti, Milan Straka, and Daniel Zeman. 2017. CoNLL 2017 shared task - automatically annotated raw texts and word embeddings. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics, Charles University.

Guy Halawi, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. 2012. Large-scale learning of word relatedness with constraints. In Proceedings of SIGKDD’12, pages 1406–1414. ACM.

Felix Hill, Roi Reichart, and Anna Korhonen. 2016. Simlex-999: Evaluating semantic models with (genuine) similarity estimation. Computational Linguistics.

Lingpeng Kong, Chris Alberti, Daniel Andor, Ivan Bogatyy, and David Weiss. 2017. Dragnn: A transition-based framework for dynamically connected neural networks. arXiv preprint arXiv:1703.04474.

Omer Levy and Yoav Goldberg. 2014. Dependencybased word embeddings. In Proceedings of ACL.

Thang Luong, Richard Socher, and Christopher D Manning. 2013. Better word representations with recursive neural networks for morphology. In Proceedings of CoNLL’13, pages 104–113.

Juhani Luotolahti, Jenna Kanerva, Veronika Laippala, Sampo Pyysalo, and Filip Ginter. 2015. Towards universal web parsebanks. In Proceedings of the International Conference on Dependency Linguistics (Depling’15), pages 211–220. Uppsala University.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. In Proceedings of ICLR.

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Proceedings of NIPS, pages 3111–3119.

George A Miller and Walter G Charles. 1991. Contextual correlates of semantic similarity. Language and cognitive processes, 6(1):1–28.

Thomas Müller, Helmut Schmid, and Hinrich Schütze. 2013. Efficient higher-order crfs for morphological tagging. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 322–332.

Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, et al. 2016. Universal dependencies v1: A multilingual treebank collection. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), pages 1659–1666.

Tommi Pirinen. 2008. Suomen kielen äärellistilainen automaattinen morfologinen jäsennin avoimen lähdekoodin resurssein. University of Helsinki. Sampo Pyysalo, Jenna Kanerva, Anna Missilä, Veronika Laippala, and Filip Ginter. 2015. Universal dependencies for Finnish. In Proceedings of NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania, pages 163–172.

Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. 2011. A word at a time: computing word relatedness using temporal semantic analysis. In Proceedings of WWW’11, pages 337–346.

Herbert Rubenstein and John B Goodenough. 1965. Contextual correlates of synonymy. Communications of the ACM, 8(10):627–633.

Milan Straka, Jan Hajic, Jana Straková, and Jan Hajic jr. 2015. Parsing universal dependency treebanks using neural networks and search-based oracle. In Proceedings of Fourteenth International Workshop on Treebanks and Linguistic Theories (TLT 14), December.

Milan Straka, Jan Hajic, and Jana Straková. 2016. UDPipe: trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, POS tagging and parsing. In Proceedings of LREC’16.

Jana Straková, Milan Straka, and Jan Hajic. 2014. Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 13–18, Baltimore, Maryland, June. Association for Computational Linguistics.

Dongqiang Yang and David MW Powers. 2006. Verb similarity on the taxonomy of wordnet. In Proceedings of GWC’06.

Citations in Crossref