Learnability-based Syntactic Annotation Design
Roy Schwartz, Omri Abend and Ari Rappoport
The Hebrew University
In proceedings of COLING 2012
Learnability-based Syntactic Annotation Design Roy Schwartz, Omri - - PowerPoint PPT Presentation
Learnability-based Syntactic Annotation Design Roy Schwartz, Omri Abend and Ari Rappoport The Hebrew University In proceedings of COLING 2012 Overview In many cases, there is more than one plausible way to annotate syntactic structures
In proceedings of COLING 2012
– A single annotation must be selected
– A principled learnability-based methodology – Use parsers for annotation design
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– More than 40% of the tokens in PTB participate in at least one VSS*
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* Schwartz et al., ACL 2011
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– More than 40% of the tokens in PTB participate in at least one VSS*
– Different parsers train and evaluate against different annotation schemes
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* Schwartz et al., ACL 2011 ** Nilsson et al., ACL 2006
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– Usually the direction of a single edge
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– Usually the direction of a single edge
– Not each VSS alone
– The way in which the VSS attaches to the rest of the sentence – These can lead to performance differences
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(McDonald et al. 2005)
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(McDonald et al. 2005)
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(McDonald et al. 2005)
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(McDonald et al. 2005)
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– Namely, how easy it is to learn a given annotation scheme using statistical tools
– Training on more learnable schemes results in more accurate parsers
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– Learnability using distributional methods has been used as an important consideration in designing the phrase structure formalism* – It is also used recurrently in cognitive science**
* Chomsky 2006 ** Chater and Vitányi 2003, Perfors et al. 2011
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parser1 parser2 parser3 result1 result2 result3 annotation scheme corpus
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parser1 parser2 parser3 result1 result2 result3 annotation scheme corpus
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Learnability-based Syntactic Annotation Design @ Schwartz, Abend and Rappoport, COLING 2012
parser1 parser2 parser3 result1 result2 result3 annotation scheme corpus parser result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 parser2 parser3 result1 result2 result3 annotation scheme corpus parser result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3
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scheme1
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3 parser2 result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3 parser2 result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3 parser2 result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser3 result1 result2 result3 corpus scheme1 scheme2 scheme3
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parser1 result1 result2 result3 corpus scheme1 scheme2 scheme3 parser2 result1 result2 result3 corpus scheme1 scheme2 scheme3
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(a) Prepositional Phrases (b) Noun Phrases (c) Coordinations (f) Infinitive Verbs (e) Noun Sequences (d) Verb Groups
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– Graph based parsers
– Transition based parsers
– Other
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– Prepositions (and not NPs) as heads of PPs – Nouns (and not their determiners) as heads of NPs – Conjuncts as heads of coordination structures
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– Prepositions (and not NPs) as heads of PPs – Nouns (and not their determiners) as heads of NPs – Conjuncts as heads of coordination structures
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– Up to 19.8% error reduction for a single structure
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– Up to 19.8% error reduction for a single structure
– Selecting the more learnable annotation in all 3 VSSs results in an even more learnable scheme – Up to 35.3% error reduction by selecting the most vs. least learnable annotation
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– POS tagging , Phrase Structure parsing, etc.
– Ballesteros and Nivre, CL 2013
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– Some annotations are clearly more learnable than others
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– Some annotations are clearly more learnable than others
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– Some annotations are clearly more learnable than others
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– Sometimes you have to choose
– A principled learnability-based methodology – Use parsers as research tools
– Parser independent – up to 35.3% error reduction
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– Sometimes you have to choose
– A principled learnability-based methodology – Use parsers as research tools
– Parser independent – up to 35.3% error reduction
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