WordNet: a lexical database for English
Communications of the ACM
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic predicate argument analysis of the Penn TreeBank
HLT '01 Proceedings of the first international conference on Human language technology research
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Deriving generalized knowledge from corpora using WordNet abstraction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Automatic fine-grained semantic classification for domain adaptation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Open knowledge extraction through compositional language processing
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Analysis of a probabilistic model of redundancy in unsupervised information extraction
Artificial Intelligence
Deterministic statistical mapping of sentences to underspecified semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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The Penn Treebank encodes valuable information such as grammatical function, semantic roles, and identification of traces. The addition of such information was intended to facilitate the process of predicate-argument extraction. However, even with the enriched annotation this task is far from trivial and, to our knowledge, no complete set of predicate argument structures derived from the Treebank exists. Our paper describes a method for retrieving predicate-argument structures that circumvents the complexity of the tree structures in the corpus, while employing few template rules. Our system operates on a flattened, morphologically enriched version of the corpus. This flattened representation allows access to all levels of the tree simultaneously and thus enables the detection of the main sentence constituents by means of simple template rules. A small number of rules apply to identify the head words of each constituent and the latter fill in the constituent templates, to build the logical forms representative of the predicate argument structure. The system is robust in the face of incomplete syntactic coverage.