Structured Parameter Estimation for LFG-DOP using Backoff (2003)
| Venue: | IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING (RANLP’03 |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Hearne03structuredparameter,
author = {Mary Hearne and et al.},
title = {Structured Parameter Estimation for LFG-DOP using Backoff},
booktitle = {IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING (RANLP’03},
year = {2003},
pages = {184--191},
publisher = {}
}
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Abstract
Despite its state-of-the-art performance, the Data Oriented Parsing (DOP) model has been shown to suffer from biased parameter estimation, and the good performance seems more the result of ad hoc adjustments than correct probabilistic generalization over the data. In recent work, we developed a new estimation procedure, called Backoff Estimation, for DOP models that are based on Phrase-Structure annotations (so called Tree-DOP models). Backoff Estimation deviates from earlier methods in that it treats the model parameters as a highly structured space of correlated events (backoffs), rather than a set of disjoint events. In this paper we show that the problem of biased estimates also holds for DOP models that are based on Lexical-Functional Grammar annotations (i.e. LFG-DOP), and that the LFG-DOP parameters also constitute a hierarchically structured space. Subsequently
