@inproceedings{deutsch-etal-2018-distributional,
title = "A Distributional and Orthographic Aggregation Model for {E}nglish Derivational Morphology",
author = "Deutsch, Daniel and
Hewitt, John and
Roth, Dan",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1180",
doi = "10.18653/v1/P18-1180",
pages = "1938--1947",
abstract = "Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering. In this work, we tackle the task of derived word generation. That is, we attempt to generate the word {``}runner{''} for {``}someone who runs.{''} We identify two key problems in generating derived words from root words and transformations. We contribute a novel aggregation model of derived word generation that learns derivational transformations both as orthographic functions using sequence-to-sequence models and as functions in distributional word embedding space. The model then learns to choose between the hypothesis of each system. We also present two ways of incorporating corpus information into derived word generation.",
}
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%0 Conference Proceedings
%T A Distributional and Orthographic Aggregation Model for English Derivational Morphology
%A Deutsch, Daniel
%A Hewitt, John
%A Roth, Dan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F deutsch-etal-2018-distributional
%X Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering. In this work, we tackle the task of derived word generation. That is, we attempt to generate the word “runner” for “someone who runs.” We identify two key problems in generating derived words from root words and transformations. We contribute a novel aggregation model of derived word generation that learns derivational transformations both as orthographic functions using sequence-to-sequence models and as functions in distributional word embedding space. The model then learns to choose between the hypothesis of each system. We also present two ways of incorporating corpus information into derived word generation.
%R 10.18653/v1/P18-1180
%U https://aclanthology.org/P18-1180
%U https://doi.org/10.18653/v1/P18-1180
%P 1938-1947
Markdown (Informal)
[A Distributional and Orthographic Aggregation Model for English Derivational Morphology](https://aclanthology.org/P18-1180) (Deutsch et al., ACL 2018)
ACL