In this paper we present a language resource for German, composed of a list of 1,021 unique errors extracted from a collection of texts written by people with dyslexia. The errors were annotated with a set of linguistic characteristics as well as visual and phonetic features. We present the compilation and the annotation criteria for the different types of dyslexic errors. This language resource has many potential uses since errors written by people with dyslexia reflect their difficulties. For instance, it has already been used to design language exercises to treat dyslexia in German. To the best of our knowledge, this is first resource of this kind in German.

In this work we introduce and describe a language resource composed of lists of simpler synonyms for Spanish. The synonyms are divided in different senses taken from the Spanish OpenThesaurus, where context disambiguation was performed by using statistical information from the Web and Google Books Ngrams. This resource is freely available online and can be used for different NLP tasks such as lexical simplification. Indeed, so far it has been already integrated into four tools.

We introduce a language resource for Spanish, DysList, composed of a list of unique 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 visual and phonetic features. To the best of our knowledge this is the largest resource of this kind, especially given the difficulty of finding texts written by people with dyslexia

This paper presents a comparable corpus of Portuguese and Spanish consisting of legal and health texts. We describe the annotation of zero subject, impersonal constructions and explicit subjects in the corpus. We annotated 12,492 examples using a scheme that distinguishes between different linguistic levels (phonology, syntax, semantics, etc.) and present a taxonomy of instances on which annotators disagree. The high level of inter-annotator agreement (83%-95%) and the performance of learning algorithms trained on the corpus show that our corpus is a reliable and useful resource.