A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
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DOI：
10.18653/v1/d18-1410
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发表时间：
2018-10
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影响因子：
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通讯作者：
Mounica Maddela;W. Xu
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文献类型：
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作者：
Mounica Maddela;W. Xu
目前的词汇简化方法严重依赖于语法和语料库水平的功能，并不总是符合人类的判断。我们创建了一个包含15，000个英语单词的人类评级单词复杂度词典，并提出了一种新的神经可读性排名模型，该模型具有基于高斯的特征向量化层，该层利用这些人类评级来衡量任何给定单词或短语的复杂度。对于不同的词汇简化任务和评估数据集，我们的模型比最先进的系统表现得更好。此外，我们还产生SimplePPDB++，超过1000万简化释义规则的词汇资源，通过将我们的模型应用到释义数据库（PPDB）。
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).