Unsupervised Learning of Morphological Forests
Name
tacl_a_00066.pdf
Description
Published version
Size
1.71 MB
Format
Adobe PDF
Checksum (MD5)
c44e6d03254dc96fa43ba9059cfa1ce2
Author(s)
Luo, Jiaming
Narasimhan, Karthik
Barzilay, Regina
Date Issued
2017
Journal
Transactions of the Association for Computational Linguistics
Publisher
MIT Press - Journals
Version
Final published version
Abstract
This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families, and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1162/TACL_A_00066
