The Stanford Typed Dependencies Representation

Summary

The paper describes Stanford typed dependencies (SD), a dependency scheme designed to make parser output usable by people outside linguistics who need to extract relations from text. It states the design principles: every relation is a binary link between two words, relations should be meaningful and based on traditional grammar, content words should be linked rather than function words where possible, and labels form a hierarchy so that a more general label can be used when the specific one is unclear. SD is compared with the GR and PARC schemes on shared-task sentences; it distinguishes more relation types, makes content words heads (including in copular constructions), and in its collapsed form folds prepositions and coordination into direct links between content words. The authors argue that SD is a suitable gold standard for parser evaluation, closer to downstream needs than bracket-based scores. SD became a de facto standard and the basis of Universal Dependencies.

How Mova uses it

• xcomp has no subject of its own — cited (via Mova's notes on English treebank history) for the observation that control was lost in older SD conversions, as background to the rule en.errors.xcomp-no-subject (an xcomp must not have its own subject; a clause with its own subject is ccomp).
• More generally, SD is the historical source of conventions that old converters and models still reproduce (e.g. cc on the first conjunct, labels like dobj, nsubjpass), which Mova's label registry rejects.

Effectiveness in Mova

Background reading for one seed rule and for understanding legacy conventions. Not measured separately.

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Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{marneffe2008stanford,
  title = {{The Stanford Typed Dependencies Representation}},
  author = {Marneffe, Marie-Catherine de and Manning, Christopher D.},
  booktitle = {Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation},
  year = {2008},
  url = {https://aclanthology.org/W08-1301/}
}

References

36 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Airola, Antti, Sampo Pyysalo, Jari Björne, Tapio Pahikkala, Filip Ginter, and Tapio Salakoski. 2008. A graph kernel for protein-protein interaction extraction. In Proceedings of BioNLP 2008: Current Trends in Biomedical Natural Language Processing (ACL08). find
2. Banko, Michele, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. 2007. Open information extraction from the web. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007). find
3. Black, E., S. Abney, D. Flickinger, C. Gdaniec, R. Grishman, P. Harrison, D. Hindle, R. Ingria, F. Jelinek, J. Klavans, M. Liberman, M. Marcus, S. Roukos, B. Santorini, and T. Strzalkowski. 1991. A procedure for quantitatively comparing the syntactic coverage of English grammars. In Proceedings, Speech and Natural Language Workshop, pages 306–311, Pacific Grove, CA. DARPA. find
4. Bresnan, Joan. 2001. Blackwell, Oxford. Lexical-Functional Syntax. find
5. Carroll, John, Guido Minnen, and Ted Briscoe. 1999. Corpus annotation for parser evaluation. In Proceedings of the EACL workshop on Linguistically Interpreted Corpora (LINC). find our card
6. Clegg, Andrew B. and Adrian J. Shepherd. 2007. Benchmarking natural-language parsers for biological applications using dependency graphs. BMC Bioinformatics, 8:24. find
7. Clegg, Andrew B. 2008. Computational-Linguistic Approaches to Biological Text Mining. Ph.D. thesis, School of Crystallography, Birkbeck, University of London. find
8. Dagan, Ido, Oren Glickman, and Bernardo Magnini. 2006. The PASCAL recognising textual entailment challenge. In et al., Quinonero-Candela, editor, MLCW 2005, LNAI Volume 3944, pages 177–190. Springer-Verlag. find
9. de Marneffe, Marie-Catherine, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of LREC-06. find our card
10. Erkan, Gunes, Arzucan Ozgur, and Dragomir R. Radev. 2007. Semi-supervised classification for extracting protein interaction sentences using dependency parsing. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). find
11. Fellbaum, Christiane. 1998. WordNet: an electronic lexical database. MIT Press. find
12. Fillmore, Charles J., Christopher R. Johnson, and Miriam R.L. Petruck. 2003. Background to FrameNet. International Journal of Lexicography, 16:235–250. find
13. Fundel, Katrin, Robert Küffner, and Ralf Zimmer. 2007. RelEx relation extraction using dependency parse trees. Bioinformatics, 23. find
14. Giampiccolo, Danilo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The third PASCAL recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pages 1–9. find
15. Greenwood, Mark A. and Mark Stevenson. 2007. A semi-supervised approach to learning relevant protein-protein interaction articles. In Proceedings of the Second BioCreAtIvE Challenge Workshop, Madrid, Spain. find
16. King, Tracy H., Richard Crouch, Stefan Riezler, Mary Dalrymple, and Ronald Kaplan. 2003. The PARC 700 dependency bank. In 4th International Workshop on Linguistically Interpreted Corpora (LINC-03). find
17. Klein, Dan and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Meeting of the Association for Computational Linguistics. find our card
18. Lease, Matthew and Eugene Charniak. 2005. Parsing biomedical literature. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP’05). find
19. Levy, Roger and Galen Andrew. 2006. Tregex and Tsurgeon: tools for querying and manipulating tree data structures. In LREC 2006. http://www-nlp.stanford.edu/software/tregex.shtml. ↗
20. Levy, Roger and Christopher D. Manning. 2004. Deep dependencies from context-free statistical parsers: correcting the surface dependency approximation. In ACL 42, pages 328–335. find
21. Lin, Dekang and Patrick Pantel. 2001. Discovery of inference rules for question answering. Natural Language Engineering, 7(4):343–360. find
22. Lin, Dekang. 1998. Dependency-based evaluation of MINIPAR. In Workshop on the Evaluation of Parsing Systems, Granada, Spain. find
23. Marcus, Mitchell P., Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of english: The Penn Treebank. Computational Linguistics, 19 (2). find our card
24. Meena, Arun and T. V. Prabhakar. 2007. Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In Advances in Information Retrieval, volume 4425 of Lecture Notes in Computer Science. Springer. find
25. Moldovan, Dan I. and Vasile Rus. 2001. Logic form transformation of wordnet and its applicability to question answering. In Meeting of the Association for Computational Linguistics, pages 394–401. find
26. Mollá, Diego and Ben Hutchinson. 2003. Intrinsic versus extrinsic evaluations of parsing systems. In Proceedings of the Workshop on Evaluation Initiatives in Natural Language Processing, pages 43–50. European Association for Computational Linguistics. find
27. Pyysalo, Sampo, Filip Ginter, Katri Haverinen, Juho Heimonen, Tapio Salakoski, and Veronika Laippala. 2007a. On the unification of syntactic annotations under the Stanford dependency scheme: A case study on BioInfer and GENIA. In Proceedings of BioNLP 2007: Biological, translational, and clinical language processing (ACL07). find
28. Pyysalo, Sampo, Filip Ginter, Juho Heimonen, Jari Björne, Jorma Boberg, Jouni Järvinen, and Tapio Salakoski. 2007b. BioInfer: A corpus for information extraction in the biomedical domain. BMC Bioinformatics, 8:50. find
29. Sagae, Kenji, Yusuke Miyao, and Jun’ichi Tsujii. 2008. Challenges in mapping of syntactic representations for framework-independent parser evaluation. In Proceedings of the Workshop on Automated Syntatic Annotations for Interoperable Language Resources at the First International Conference on Global Interoperability for Language Resources (ICGL’08). find
30. Sleator, Daniel D. and Davy Temperley. 1993. Parsing English with a link grammar. In Third International Workshop on Parsing Technologies. find our card
31. Snow, Rion, Daniel Jurafsky, and Andrew Y. Ng. 2005. Learning syntactic patterns for automatic hypernym discovery. In Proceedings of NIPS 2004. find
32. Urbain, Jay, Nazli Goharian, and Ophir Frieder. 2007. find
33. IIT TREC 2007 genomics track: Using concept-based semantics in context for genomics literature passage retrieval. In The Sixteenth Text REtrieval Conference (TREC 2007) Proceedings. find
34. Zhuang, Li, Feng Jing, Xiao yan Zhu, and Lei Zhang. 2006. Movie review mining and summarization. In Proc. ACM Conference on Information and Knowledge Management (CIKM). find
35. Zouaq, Amal, Roger Nkambou, and Claude Frasson. 2006. The knowledge puzzle: An integrated approach of intelligent tutoring systems and knowledge management. In Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), pages 575–582. find
36. Zouaq, Amal, Roger Nkambou, and Claude Frasson. 2007. Building domain ontologies from text for educational purposes. In Proceedings of the Second European Conference on Technology Enhanced Learning: Creating new learning experiences on a global scale. find

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