Abstract
Lexical Simplification is the task of replacing individual words of a text with words that are easier to understand, so that the text as a whole becomes easier to comprehend, e.g. by people with learning disabilities or by children who learn to read.
Although this seems like a straightforward task, evaluating algorithms for this task is not so. The problem is how to build a dataset that provides an exhaustive list of easier to understand words in different contexts, and to obtain an absolute ordering on this list of synonymous expressions.
In this paper we reuse existing resources for a similar problem, that of Lexical Substitution, and transform this dataset into a dataset for Lexical Simplification. This new dataset contains 430 sentences, with in each sentence one word marked. For that word, a list of words that can replace it, sorted by their difficulty, is provided. The paper reports on how this dataset was created based on the annotations of different persons, and their agreement. In addition we provide several metrics for computing the similarity between ranked lexical substitutions, which are used to assess the value of the different annotations, but which can also be used to compare the lexical simplifications suggested by an algorithm with the ground truth model.
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De Belder, J., Moens, MF. (2012). A Dataset for the Evaluation of Lexical Simplification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_36
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DOI: https://doi.org/10.1007/978-3-642-28601-8_36
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