Light verb constructions consist of a verbal and a nominal component, where the noun preserves its original meaning while the verb has lost it (to some degree). They are syntactically flexible and their meaning can only be partially computed on the basis of the meaning of their parts, thus they require special treatment in natural language processing. For this purpose, the first step is to identify light verb constructions.In this study, we present our conditional random fields-based tool-called FXTagger-for identifying light verb constructions. The flexibility of the tool is demonstrated on two, typologically different, languages, namely, English and Hungarian. As earlier studies labeled different linguistic phenomena as light verb constructions, we first present a linguistics-based classification of light verb constructions and then show that FXTagger is able to identify different classes of light verb constructions in both languages.Different types of texts may contain different types of light verb constructions; moreover, the frequency of light verb constructions may differ from domain to domain. Hence we focus on the portability of models trained on different corpora, and we also investigate the effect of simple domain adaptation techniques to reduce the gap between the domains. Our results show that in spite of domain specificities, out-domain data can also contribute to the successful LVC detection in all domains.

Abstract. Recruiting employees is a serious issue for many enterprises. We propose here a procedure to automatically analyse uploaded CVs then prefill the application form which can save a considerable amount of time for applicants thus it increases user satisfaction. For this purpose, we shall introduce a high-recall CV parsing system for Hungarian, English and German. We comparatively evaluate two approaches for providing training data to our machine learning machinery and discuss other experiences gained.