Experiments with Generative Models for Dependency Tree Linearization
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Gibson_Experiments with.pdf
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Author(s)
Futrell, Richard Landy Jones
Gibson, Edward A.
Date Issued
September 2015
Journal
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Citation
Futrell, Richard, and Edward Gibson. "Experiments with Generative Models for Dependency Tree Linearization." 2015 Conference on Empirical Methods in Natural Language Processing (September 2015).
Version
Author's final manuscript
Abstract
We present experiments with generative models for linearization of unordered labeled syntactic dependency trees (Belz et al., 2011; Rajkumar and White, 2014). Our linearization models are derived from generative models for dependency structure (Eisner, 1996). We present a series of generative dependency models designed to capture successively more information about ordering constraints among sister dependents. We give a dynamic programming algorithm for computing the conditional probability of word orders given tree structures under these models. The models are tested on corpora of 11 languages using test-set likelihood, and human ratings for generated forms are collected for English. Our models benefit from representing local order constraints among sisters and from backing off to less sparse distributions, including distributions not conditioned on the head.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.cs.cmu.edu/~ark/EMNLP-2015/accepted-papers-noabstract.html
