Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula>, an NLP application designed to extract <inline-formula><tex-math notation="LaTeX">\mathsf{MedDRA}</tex-math></inline-formula> terms from narrative clinical text. Given a narrative description, <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula>. Moreover, we do a change of language, moving to English documents. In particular, we tested <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.