Inducing Probabilistic CCG Grammars from Logical Form with Higher-order Unification
Authors: Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman
Venue: EMNLP 2010
Leader: Weisi
Reminders:
- Leave a comment on this post (non-anonymously) giving the details of the related paper you will read (include a URL), by Monday, January 17.
- Post your commentary (a paragraph) as a new blog post, by Wednesday, January 19.
- If you haven't received a message from the 11-713 mailing list listing the schedule for leading discussions, let Tae know.
I think I plan to read this review of the CCG formalism.
ReplyDeleteMark Steedman and Jason Baldridge. Combinatory Categorial Grammar. To appear in Robert Borsley and Kersti Borjars (eds.) Constraint-based approaches to grammar: alternatives to transformational syntax. Oxford: Blackwell. PDF (Will appear in February 2011.)
http://comp.ling.utexas.edu/jbaldrid/papers/SteedmanBaldridgeNTSyntax.pdf
Mark Steedman's website claims a different citation "forthcoming 2007" for a differently titled book, but that book appears not to have ever existed.
I plan to read the following paper in order to learn about one of the other methods for solving this problem.
ReplyDeleteLu et al. A generative model for parsing natural language to meaning representations. EMNLP 2008.
http://www.cs.washington.edu/homes/lsz/papers/lnlz-emnlp08.pdf
I plan to read:
ReplyDeleteUnsupervised Semantic Parsing
Hoifung Poon, Pedro Domingos, EMNLP 2009
http://aclweb.org/anthology/D/D09/D09-1001.pdf
Similar goal (mapping text to formal meaning representations), but different setting (unsupervised versus supervised).
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ReplyDeleteI will read the following paper (by one of the co-authors):
ReplyDeleteZettlemoyer, L.S. & Collins, M.
Learning context-dependent mappings from sentences to logical form
http://people.csail.mit.edu/mcollins/papers/acl09.pdf
The problem addressed now deals with context-dependent mappings.
I plan to read the following paper -
ReplyDeleteClark, S. & Curran, J. R. (2007). Wide-coverage efficient
statistical parsing with CCG and log-linear models.
Computational Linguistics, 33(4), 493–552.
http://delivery.acm.org/10.1145/1120000/1119368/p97-clark.pdf
It presents a set of log linear methods for parsing CCGs and develops a strategy to build a CCG parser.
*This is a very lengthy paper (~60 pages), so I aim at reading a significant part.
This comment has been removed by the author.
ReplyDeleteThis comment has been removed by the author.
ReplyDeleteI plan to read the paper:
ReplyDeleteLuke S. Zettlemoyer, Michael Collins. Learning Context-dependent Mappings from Sentences to Logical Form. In Proceedings of the Joint Conference of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), 2009.
I am going to read one of the discussed related papers:
ReplyDeleteYuk Wah Wong; Raymond Mooney
Learning for Semantic Parsing with Statistical Machine Translation
http://www.aclweb.org/anthology/N/N06/N06-1056.pdf
This work applies established techniques from the area of statistical machine translation to the task of semantic parsing.
I pick the same paper as Michael:
ReplyDeleteYuk Wah Wong; Raymond Mooney
Learning for Semantic Parsing with Statistical Machine Translation
http://www.aclweb.org/anthology/N/N06/N06-1056.pdf
To be honest, I pick it because I'm interested in SMT techniques and want to see how they can relate to the topic discussed in our focus paper.
"Using string-kernels for learning semantic parsers"
ReplyDeleteLink: http://portal.acm.org/citation.cfm?id=1220290
With limited experience in semantic parsing, I selected this paper from the references of this week's primary paper. It learns a SVM classifier for each possible production in the semantic grammar. These are used to compose entire semantic derivations. The authors claim it is robust against "noise."
I am planning to read:
ReplyDeleteBos, J., Clark, S., Steedman, M., Curran, J. R., & Hockenmaier, J. (2004). Wide-coverage semantic representations from a CCG parser. In Proceedings of the International Conference on Computational Linguistics
Which looks like an interesting, earlier result using similar techniques.