Abstract Meaning Representation for Sembanking

What it is about

The paper presents Abstract Meaning Representation (AMR), a rooted, labelled graph for the meaning of whole sentences, meant to be easy for people to read and for programs to traverse. Concepts are English words, PropBank frames such as describe-01, or keywords for entities, dates and quantities. Edges are about a hundred relations, mostly PropBank arguments. The design aims at one graph per meaning, so that a passive, a nominal and a verbal form of the same sentence receive the same graph. Articles and tense are left out, and the authors state that AMR is English-biased and is not an interlingua. The paper describes a bank of several thousand sentences, annotated by trained annotators at about 7 to 10 minutes per sentence from scratch and 1 to 3 minutes for post-editing. It reports no parsing experiment.

Ideas

• I1. Function words are not concepts. AMR drops articles and tense from the graph and keeps only their semantic role. Keep determiners, tense and articles as features of the meta token (open problem 4), and not as tokens of their own.
• I2. Role-keyed merges: a concept merge must keep the argument roles of its members (arg0, arg1). A be/have merge fails this check, so a role test can block it without a list of banned pairs.
• I3. Score meta-worlds as sets of triples (concept, role, concept) with a Smatch-style F-score, reported beside exact match, so that a reading with one wrong relation is not scored as zero.

For Mova

The code is tools/translate/meta.rs and hebrew.rs. I1 and I2 address open problems 2 and 4. The graph format itself is not for Mova, but PropBank-keyed frames fit the frames that prag already builds, which makes the role check in I2 easy to write.

Topics: Semantic role labeling · Machine translation

Cite

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

@inproceedings{banarescu2013abstract,
  title = {{Abstract Meaning Representation for Sembanking}},
  author = {Banarescu, Laura and Bonial, Claire and Cai, Shu and Georgescu, Madalina and Griffitt, Kira and Hermjakob, Ulf and Knight, Kevin and Koehn, Philipp and Palmer, Martha and Schneider, Nathan},
  booktitle = {Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse},
  year = {2013},
  url = {https://aclanthology.org/W13-2322/}
}

References

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

1. O. Abend and A. Rappoport. 2013. UCCA: A semantics-based grammatical annotation scheme. In Proc. IWCS. find
2. C. Baker, C. Fillmore, and J. Lowe. 1998. The Berkeley FrameNet project. In Proc. COLING. find our card
3. V. Basile, J. Bos, K. Evang, and N. Venhuizen. 2012a. Developing a large semantically annotated corpus. In Proc. LREC. Disjunctive AMR guideline: amr.isi.edu/damr.1.0.pdf V. Basile, J. Bos, K. Evang, and N. Venhuizen. 2012b. A platform for collaborative semantic annotation. In Proc. EACL demonstrations. find
4. A. Böhmová, J. Hajič, E. Hajičová, and B. Hladká. 2003. The Prague dependency treebank. In Treebanks. Springer. find
5. A. Butler and K. Yoshimoto. 2012. Banking meaning representations from treebanks. Linguistic Issues in Language Technology, 7. find
6. S. Cai and K. Knight. 2013. Smatch: An accuracy metric for abstract meaning representations. In Proc. ACL. find
7. D. Chiang, J. Andreas, D. Bauer, K. M. Hermann, B. Jones, and K. Knight. 2013. Parsing graphs with hyperedge replacement grammars. In Proc. ACL. find
8. D. Davidson. 1969. The individuation of events. In N. Rescher, editor, Essays in Honor of Carl G. Hempel. D. Reidel, Dordrecht. find
9. M. Dreyer and D. Marcu. 2012. Hyter: Meaning-equivalent semantics for translation evaluation. In Proc. NAACL. find
10. B. Jones, J. Andreas, D. Bauer, K. M. Hermann, and K. Knight. 2012. Semantics-based machine translation with hyperedge replacement grammars. In Proc. COLING. find our card
11. H. Kamp, J. Van Genabith, and U. Reyle. 2011. Discourse representation theory. In Handbook of philosophical logic, pages 125–394. Springer. find
12. P. Kingsbury and M. Palmer. 2002. From TreeBank to PropBank. In Proc. LREC. find our card
13. D. B. Lenat. 1995. Cyc: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11). find
14. R. Martins. 2012. Le Petit Prince in UNL. In Proc. LREC. find
15. C. M. I. M. Matthiessen and J. A. Bateman. 1991. Text Generation and Systemic-Functional Linguistics. Pinter, London. find
16. A. Meyers, R. Reeves, C. Macleod, R. Szekely, V. Zielinska, B. Young, and R. Grishman. 2004. The NomBank project: An interim report. In HLT-NAACL 2004 workshop: Frontiers in corpus annotation. find
17. M. Palmer, D. Gildea, and P. Kingsbury. 2005. The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1). find our card
18. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In ACL, Philadelphia, PA. find our card
19. S. Pradhan, E. Hovy, M. Marcus, M. Palmer, L. Ramshaw, and R. Weischedel. 2007. Ontonotes: A unified relational semantic representation. International Journal of Semantic Computing (IJSC), 1(4). find
20. D. Quernheim and K. Knight. 2012a. DAGGER: A toolkit for automata on directed acyclic graphs. In Proc. FSMNLP. find
21. D. Quernheim and K. Knight. 2012b. Towards probabilistic acceptors and transducers for feature structures. In Proc. SSST Workshop. find
22. K. Schuler. 2005. VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. thesis, University of Pennsylvania. find
23. S. Shieber, F. C. N. Pereira, L. Karttunen, and M. Kay. 1986. Compilation of papers on unification-based grammar formalisms. Technical Report CSLI-86-48, Center for the Study of Language and Information, Stanford, California. find
24. H. Uchida, M. Zhu, and T. Della Senta. 1996. UNL: Universal Networking Language—an electronic language for communication, understanding and collaboration. Technical report, IAS/UNU Tokyo. find
25. H. Uchida, M. Zhu, and T. Della Senta. 1999. A gift for a millennium. Technical report, IAS/UNU Tokyo. find
26. N. Venhuizen, V. Basile, K. Evang, and J. Bos. 2013. Gamification for word sense labeling. In Proc. IWCS. find
27. R. Weischedel, E. Hovy, M. Marcus, M. Palmer, R. Belvin, S. Pradhan, L. Ramshaw, and N. Xue. 2011. OntoNotes: A large training corpus for enhanced processing. In J. Olive, C. Christianson, and J. McCary, editors, Handbook of Natural Language Processing and Machine Translation. Springer. find
28. Y. W. Wong and R. J. Mooney. 2006. Learning for semantic parsing with statistical machine translation. In Proc. HLT-NAACL. find

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/banarescu-2013-abstract-meaning-representation-sembanking/