Abstract
One major reason that readability checkers are still far away from judging the understandability of texts consists in the fact that no semantic information is used. Syntactic, lexical, or morphological information can only give limited access for estimating the cognitive difficulties for a human being to comprehend a text. In this paper however, we present a readability checker which uses semantic information in addition. This information is represented as semantic networks and is derived by a deep syntactico-semantic analysis. We investigate in which situations a semantic readability indicator can lead to superior results in comparison with ordinary surface indicators like sentence length. Finally, we compute the weights of our semantic indicators in the readability function based on the user ratings collected in an online evaluation.
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vor der Brück, T., Hartrumpf, S. (2009). A Readability Checker Based on Deep Semantic Indicators. In: Vetulani, Z., Uszkoreit, H. (eds) Human Language Technology. Challenges of the Information Society. LTC 2007. Lecture Notes in Computer Science(), vol 5603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04235-5_20
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DOI: https://doi.org/10.1007/978-3-642-04235-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04234-8
Online ISBN: 978-3-642-04235-5
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