A Maximum Entropy Model for Prepositional Phrase Attachment
نویسندگان
چکیده
For this example, a human annotator's attachment decision, which for our purposes is the "correct" attachment, is to the noun phrase. We present in this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment.
منابع مشابه
Reduction of Maximum Entropy Models to Hidden Markov Models
Maximum Entropy (maxent) models are an attractive formalism for statistical models of many types and have been used for a number of purposes, including language modeling (Rosenfeld 1994), part of speech tagging (Ratnaparkhi 1996), prepositional phrase attachment (Ratnaparkhi 1998), sentence breaking (Reynar and Ratnaparkhi 1997) and parsing (Ratnaparkhi 1997). Maxent models allow the combinatio...
متن کاملHybrid Disambiguation of Prepositional Phrase Attachment and Interpretation
In this paper, a hybrid disambiguation method for the prepositional phrase (PP) attachment and interpretation problem is presented. 1 The data needed, semantic PP interpretation rules and an annotated corpus, is described first. Then the three major steps of the disambiguation method are: explained. Cross-validated evaluation results', for German (88.6-94.4% correct for binary attachment ambigu...
متن کاملLeveraging a Semantically Annotated Corpus to Disambiguate Prepositional Phrase Attachment
Accurate parse ranking requires semantic information, since a sentence may have many candidate parses involving common syntactic constructions. In this paper, we propose a probabilistic framework for incorporating distributional semantic information into a maximum entropy parser. Furthermore, to better deal with sparse data, we use a modified version of Latent Dirichlet Allocation to smooth the...
متن کاملStatistical Models for Unsupervised Prepositional Phrase Attachment
We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains h'om raw text that is annotated with only part-oi;speech tags and morphologicM base forms, as opposed to attachment information. It is therefore...
متن کاملIntegration of Semantic and Syntactic Constraints for Structural Noun Phrase Disambiguation
A fundamental problem in Natural Language Processing is the integration of syntactic and semantic constraints. In this paper we describe a new approach for the integration of syntactic and semantic constraints which takes advantage of a learned memory model. Our model combines localist representations for the integration of constraints and distributed representations for learning semantic const...
متن کامل