Foundations of statistical natural language processing
Foundations of statistical natural language processing
Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Statistical models for unsupervised prepositional phrase attachment
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Prepositional phrase attachment without oracles
Computational Linguistics
PP-attachment disambiguation boosted by a gigantic volume of unambiguous examples
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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This paper is concerned with the possibility of quantifying and comparing the productivity of similar yet distinct syntactic constructions, predicting the likelihood of encountering unseen lexemes in their unfilled slots. Two examples are explored: variants of comparative correlative constructions (CCs, e.g. the faster the better), which are potentially very productive but in practice lexically restricted; and ambiguously attached prepositional phrases with the preposition with, which can host both large and restricted inventories of arguments under different conditions. It will be shown that different slots in different constructions are not equally likely to be occupied productively by unseen lexemes, and suggested that in some cases this can help disambiguate the underlying syntactic and semantic structure.