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A resource of errors written in Spanish by people with dyslexia and its linguistic, phonetic and visual analysis

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Abstract

In this work we introduce the analysis of DysList, a language resource for Spanish composed of a list of unique spelling errors extracted from a collection of texts written by people with dyslexia. Each of the errors was annotated with a set of characteristics as well as with visual and phonetic features. To the best of our knowledge, this is the largest resource of this kind in Spanish. We also analyzed all the features of Spanish errors and our main finding is that dyslexic errors are phonetically and visually motivated.

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Notes

  1. In some literature, dyslexia is referred to as a specific reading disability only (Vellutino et al. 2004) and dysgraphia as its written manifestation (Romani et al. 1999).

  2. This estimation was carried out taking into account primary schools in the region of Murcia, Spain.

  3. The preliminary resource was described in the Proceedings of the LREC 2102 Workshop on Natural Language Processing for Improving Textual Accessibility (NLP4ITA), 27 May, Istanbul, Turkey, pp. 22–26 (Rello et al. 2012). This work presents an enlarged collection of texts with new annotations. The preliminary analysis appeared in the Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 26–31 May, Reykjavik, Iceland, pp. 1289–1296 (Rello et al. 2014). In comparison with the LREC paper, which is focused on the language resource only, this paper extends previous work by presenting the analyses of the errors using linguistic, phonetic and visual features.

  4. Available at: grupoweb.upf.es/WRG/resources/DysWebxia/DysList_resource.csv.gz.

  5. Penfriend XL (http://www.penfriend.biz/).

  6. http://www.dcs.bbk.ac.uk/~jenny/resources.html.

  7. The examples report the correct word—the first word between parentheses—the related English translation—the second word in quotes. The erroneous word are those preceded by an asterisk ‘*’. We use the standard linguistic conventions: ‘\(\langle\) \(\rangle\)’ for graphemes, ‘/ /’ for phonemes and ‘[ ]’ for phones.

  8. The edit or Levenshtein distance (Levenshtein 1965) is the minimum number of substitutions, insertions and deletions to transform one string into another. The Damerau version (Damerau 1964) counts a transposition as a single error instead of two errors. Notice that there might be more than one solution for the transformation associated to the edit distance.

  9. Notice that a deletion in the target word is an insertion in the misspelled word and vice versa.

  10. Here we refer to all web pages written in Spanish, not the web pages from Spain. For determining whether a web page was written in Spanish, we used Google Advanced Search settings http://www.google.com/advanced_search.

  11. In Catalan, the sound [i] is always represented by the letter \(\langle\)i\(\rangle\), while in Spanish it might be also represented by \(\langle\)y\(\rangle\); moreover, \(\langle\)y\(\rangle\) in Catalan is only present in the digraph \(\langle\)ny\(\rangle\) used to represent the nasal palatal consonant [ɲ]. Thus, transfer from Catalan might explain the errors.

  12. http://grupoweb.upf.es/WRG/resources/DysWebxia/DysList_resource.csv.gz.

  13. Similarly to the corpus created by Jara for adults in Costa Rica (Jara Murillo 2013), we will create a list of errors from a control group comparable in age and Spanish variant.

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Acknowledgments

We thank Martí Mayo for computing some of the features and the first author thanks the partial funding of a doctoral fellowship (FI-DGR) of the Generalitat de Calaluyna (Government of Catalonia). We thank Yolanda Otal de la Torre, teacher and professional of CREIX—Centro de Desarrollo Infantil Barcelona for helping us to collect texts written by people with dyslexia. We also thank the anonymous reviewers for their comments.

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Authors and Affiliations

  1. HCI Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Luz Rello

  2. Web Research Group, Univ. Pompeu Fabra, Barcelona, Spain

    Ricardo Baeza-Yates

  3. Yahoo Labs, Sunnyvale, CA, USA

    Ricardo Baeza-Yates

  4. Departament de Filologia Espanyola, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain

    Joaquim Llisterri

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  1. Luz Rello
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Correspondence to Luz Rello.

Appendix: Comparing English and Spanish errors

Appendix: Comparing English and Spanish errors

We took a subgroup of texts from our corpus composed of 1075 word and performed a comparison of the error distribution with a similar one in English.

English and Spanish languages are archetypes of deep and shallow orthographies, respectively. Along an orthographic transparency scale for European languages, English appears as the language with the deepest orthography and Spanish as the second most shallow one after Finnish (Seymour et al. 2003).

In Tables 14 and 15 we compare the data of the English corpus described in Pedler (2007) with our Spanish texts. We compute the error ratio as the fraction of errors over the correctly spelt words we observe. As expected, Spanish dyslexics make less spelling errors (15 %) than English dyslexics (20 %), due to the different orthographies of their languages. However, the percentage of distinct errors is almost the same.

Table 14 Error ratio and percentage of total errors (with repetitions) and distinct errors in English and Spanish texts written by people with dyslexia
Full size table

Table 15 presents the distribution of the different types of dyslexic errors for both languages. To determine if an error was a real world error we checked its existence in the Diccionario de la lengua española (Dictionary of the Spanish Language) (Real Academia Española 2001), the standard normative dictionary for Spanish.

Table 15 Distribution of errors in English and Spanish corpora
Full size table

As expected, there is a greater percentage of multi-errors in a language with deep orthography—English—than in Spanish, e.g. *qría (creía, ‘thought’). However, first letter errors are almost two times more frequent in Spanish, e.g. *tula (ruta, ‘way’). This may look surprising according to Yannakoudakis and Fawthrop (1983), whose findings report that the first letter of a misspelling is correct in the majority of cases, but in Spanish the letter \(\langle\)h\(\rangle\) at the beginning of a word is not pronounced and this generates many more errors (4.6 %) in that position (see Table 5).

The rest of the dyslexic error types are similar in both languages. There are slightly more real-word errors in Spanish, *dijo (digo, ‘said’) or *llegada (llegaba, ‘arrived’). Simple errors are the most frequent ones in both languages. However, each error type has a different frequency. A detailed analysis of the different kind of dyslexic errors and their occurrence in the Web is given in Rello and Baeza-Yates (2012).

Even if both corpora are composed of text written by children with dyslexia in English and Spanish this comparison is not definitive because the two corpora are not fully comparable. For instance, text types and text size, among other characteristics, were not controlled. However, this comparison is still useful for cross-linguistic studies (Brunswick 2010; Goulandris 2003; Seymour et al. 2003), as a preliminary approach for a qualitative cross-linguistic comparison of dyslexic errors written in both languages, as English and Spanish present similar distributions frequencies; and, as expected, differences are due to the different orthographies of the two languages.

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Rello, L., Baeza-Yates, R. & Llisterri, J. A resource of errors written in Spanish by people with dyslexia and its linguistic, phonetic and visual analysis. Lang Resources & Evaluation 51, 379–408 (2017). https://doi.org/10.1007/s10579-015-9329-0

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