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Structural Features for Predicting the Linguistic Quality of Text

Applications to Machine Translation, Automatic Summarization and Human-Authored Text

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5790)

Abstract

Sentence structure is considered to be an important component of the overall linguistic quality of text. Yet few empirical studies have sought to characterize how and to what extent structural features determine fluency and linguistic quality. We report the results of experiments on the predictive power of syntactic phrasing statistics and other structural features for these aspects of text. Manual assessments of sentence fluency for machine translation evaluation and text quality for summarization evaluation are used as gold-standard. We find that many structural features related to phrase length are weakly but significantly correlated with fluency and classifiers based on the entire suite of structural features can achieve high accuracy in pairwise comparison of sentence fluency and in distinguishing machine translations from human translations. We also test the hypothesis that the learned models capture general fluency properties applicable to human-authored text. The results from our experiments do not support the hypothesis. At the same time structural features and models based on them prove to be robust for automatic evaluation of the linguistic quality of multi-document summaries.

Keywords

  • Noun Phrase
  • Machine Translation
  • Sentence Length
  • Computational Linguistics
  • Automatic Evaluation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Nenkova, A., Chae, J., Louis, A., Pitler, E. (2010). Structural Features for Predicting the Linguistic Quality of Text. In: Krahmer, E., Theune, M. (eds) Empirical Methods in Natural Language Generation. EACL ENLG 2009 2009. Lecture Notes in Computer Science(), vol 5790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15573-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-15573-4_12

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