Automatic Assessment of Absolute Sentence Complexity
Abstract
Lexically and syntactically simpler sentences result in shorter reading time and better understanding in many people. However, no reliable systems for automatic assessment of absolute sentence complexity have been proposed so far. Instead, the assessment is usually done manually, requiring expert human annotators. To address this problem, we first define the sentence complexity assessment as a five-level classification task, and build a ‘gold standard’ dataset. Next, we propose robust systems for sentence complexity assessment, using a novel set of features based on leveraging lexical properties of freely available corpora, and investigate the impact of the feature type and corpus size on the classification performance.
Cite
Text
Stajner et al. "Automatic Assessment of Absolute Sentence Complexity." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/572Markdown
[Stajner et al. "Automatic Assessment of Absolute Sentence Complexity." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/stajner2017ijcai-automatic/) doi:10.24963/IJCAI.2017/572BibTeX
@inproceedings{stajner2017ijcai-automatic,
title = {{Automatic Assessment of Absolute Sentence Complexity}},
author = {Stajner, Sanja and Ponzetto, Simone Paolo and Stuckenschmidt, Heiner},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4096-4102},
doi = {10.24963/IJCAI.2017/572},
url = {https://mlanthology.org/ijcai/2017/stajner2017ijcai-automatic/}
}