@inproceedings{maddela-xu-2018-word,
title = "A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification",
author = "Maddela, Mounica and
Xu, Wei",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1410",
doi = "10.18653/v1/D18-1410",
pages = "3749--3760",
abstract = "Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).",
}
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%0 Conference Proceedings
%T A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
%A Maddela, Mounica
%A Xu, Wei
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F maddela-xu-2018-word
%X Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
%R 10.18653/v1/D18-1410
%U https://aclanthology.org/D18-1410
%U https://doi.org/10.18653/v1/D18-1410
%P 3749-3760
Markdown (Informal)
[A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://aclanthology.org/D18-1410) (Maddela & Xu, EMNLP 2018)
ACL