The method of automatic term recognition based on machine learning is focused primarily on the most important quantitative term attributes. It is able to successfully identify terms and non-terms (with success rate of more than 95 %) and find characteristic features of a term as a terminological unit. A single-word term can be characterized as a word with a low frequency that occurs considerably more often in specialized texts than in non-academic texts, occurs in a small number of disciplines, its distribution in the corpus is uneven as is the distance between its two instances. A multi-word term is a collocation consisting of words with low frequency and contains at least one single-word term. The method is based on quantitative features and it makes it possible to utilize the algorithms in multiple disciplines as well as to create cross-lingual applications (verified on Czech and English).

This paper presents a specialized corpus tool GramatiKat in the context of Open Science principles, namely data sharing, which offers opportunities for original research and facilitates verifiability of research and building on previous research. The tool is designed primarily for examining grammatical categories from the quantitative point of view. It offers grammatical profiles of particular lemmas (currently 14 thousand Czech nouns) and the proportion of individual grammatical categories within a part of speech, i.e., the standard behavior of a word class. The data in GramatiKat are pre-processed, statistically evaluated, and presented in charts and tables for clarity, and they are available to other linguists, especially from fields of morphology and lexicography. This article is aimed at providing inspiration and support to corpus and non-corpus linguists with utilization and enhanced use of the existing tools and with the creation of new specialized tools available to other users.

When compiling a list of headwords, every lexicographer comes across words with an unattested representative dictionary form in the data. This study focuses on how to distinguish between the cases when this form is missing due to a lack of data and when there are some systemic or linguistic reasons. We have formulated lexicographic recommendations for different types of such ‘lacunas’ based on our research carried out on Czech written corpora. As a prerequisite, we calculated a frequency threshold to find words that should have the representative form attested in the data. Based on a manual analysis of 2,700 nouns, adjectives and verbs that do not, we drew up a classification of lacunas. The reasons for a missing dictionary form are often associated with limited collocability and non-preference for the representative grammatical category. Findings on unattested word forms also have significant implications for language potentiality.

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