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* The [http://ifarm.nl/signll/conll/ CoNLL Shared Tasks (scroll down the page)] have been running since 1999 and provide nice freely available annotated data. Phrase chunking, named entity tagging, dependency parsing, semantic role labeling and more. * Templated information extraction. [http://nlp.shef.ac.uk/dot.kom/resources.html CMU Seminar and Acquisitions datasets (Freitag)]. Also various MUC competition datasets (see e.g. old-school [http://www.isi.edu/~hobbs/fastus-schabes-jul95.pdf Hobbs 1995], or new-school [http://www-cs.stanford.edu/people/nc/pubs/acl2011-chambers-templates.pdf Chambers and Jurafsky 2011]). * Named entity recognition. e.g. [http://www.cnts.ua.ac.be/conll2002/ner/ CoNLL shared task 2002]. * Segmentation. E.g. [http://nlp.stanford.edu/~grenager/data/unsupie.tgz Classified ad segmentation data] [http://nlp.stanford.edu/~grenager (Grenager)]. Or the CoNLL chunking tasks. Discourse-topic segmentation for [http://people.csail.mit.edu/jacobe/software.html lecture transcripts (Eisenstein)]. Others? * Part-of-speech tagging. E.g. [http://www.ark.cs.cmu.edu/TweetNLP/ CMU ARK Twitter annotated POS dataset], or [http://www.archive.org/details/BrownCorpus Brown corpus], any Penn Treebank or other treebank, many (or all?) of the CoNLL datasets, etc. * Semantic role labeling. CoNLL shared task in [http://www.lsi.upc.edu/~srlconll/ 2004, 2005]. * Parsing. ** Dependency parsing: CoNLL shared tasks in [http://ilk.uvt.nl/conll/ 2006], and maybe 2007 too? ** Phrase structure parsing: an annotated dataset is called a "treebank". There are many treebanks, but the Penn Treebank is most famous. It is often used for part-of-speech tagging experiments too. ** Noah requests: do not do phrase structure parsing on the English Wall Street Journal Penn Treebank, it's been overdone. (Chinese or anything else is fine, or English dependency parsing is fine.) * Coreference. For within-document coreference, the MUC and ACE datasets are standard. For cross-document coreference, the "John Smith" dataset is [http://alias-i.com/lingpipe/demos/tutorial/cluster/read-me.html linked here]. * BioNLP: The [http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi?page=GENIA+corpus Genia corpus] has tons of syntactic and relational annotations, and an ontology too. * Logical-form semantic parsing: [http://www.cs.utexas.edu/users/ml/nldata/geoquery.html GeoQuery dataset (Mooney)] has been around for a while, and has had some interesting recent work. There's also one called the "jobs dataset" used in Liang 2011 and other papers. * Frame-semantic parsing: related to semantic role labeling, often related to FrameNet or VerbNet. There is a small amount FrameNet data (see [http://www.cs.cmu.edu/~nschneid/dscs.pdf Das et al 2010] for pointers) * Sentiment. Please remember document classification (a common form of sentiment analysis) does not count as structured prediction. However, fine-grained sentence-level or phrase-level annotation does count. E.g. the [http://www.cs.pitt.edu/mpqa/databaserelease/ MPQA opinion corpus], or [http://www.cs.cornell.edu/home/llee/data/convote.html Congressional floor debate transcripts]. * Discourse. The Congressional floor debates dataset (conversational information to predict). Discourse segmentation: lecture transcripts (above). Also, [http://www.seas.upenn.edu/~pdtb/ Penn Discourse Treebank]. * In general, the [http://www.ldc.upenn.edu/ LDC] has many linguistically annotated datasets. CMU (through the LTI) has a subscription to obtain many of them. * Some more places to check: ** On this wiki, besides the syllabus, also the [[Paper]] and [[Dataset]] pages. ** Noah's book, or the Jurafsky&Martin textbook, or even the old Manning&Schuetze textbook. Also there are dataset [http://aclweb.org/aclwiki/index.php?title=List_of_resources_by_language lists on the ACL Wiki], for example the "English Corpora" listing (though not all of these are datasets appropriate for course projects, of course.) * Many, many more possibilities! -[[User:Brendan]]

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