Morphologically Annotated Lexical Decision Data for 12,242 Czech Word Forms

Abstract

We present CzeLeD, a large-scale lexical decision dataset for Czech containing 12,242 lemmas and inflected word forms derived from the HeCz self-paced reading corpus and supplemented with pseudowords. A total of 1,977 participants contributed over 849,000 trials, accompanied by demographic data and extensive linguistic annotation, including lemma and word-form frequencies, neighborhood density, phonotactic and orthotactic probability. Alongside the trial-level dataset, we provide lexical decision norms summarizing reaction times and accuracy using a transparent and reproducible trimming procedure. Data validation drew on both response accuracy and reaction times. Participants showed high overall accuracy (94.79%), and reaction times exhibited the expected strong inverse relationship with word form frequency. Importantly, CzeLeD can be directly linked to the HeCz corpus, enabling systematic comparison between word recognition in isolation and in sentential context. By combining broad lexical coverage, rich annotation, and seamless integration with an existing context-bound processing corpus, CzeLeD offers a powerful, reusable resource for investigating lexical processing, morphological complexity, and contextual effects in Czech and beyond.