Multiword Expressions – Comparative Analysis Based on Aligned Corpora Cvetana Krstev Association for Language Resources and Technologies Belgrade, Serbia CvetanaJK@gmail.com Ranka Stankovi´c University of Belgrade F. of Mining and Geology Belgrade, Serbia ranka@rgf.bg.ac.rs Aleksandra Markovi´c Institute for the Serbian Language SASA Belgrade, Serbia malexa39@gmail.com Relevant UniDive working groups: WG1, WG2 1 Introduction The aim of our paper is to research inter- and intra- linguistic similarities/differences in the use of sim- ile rhetorical figures (Niculae and Yaneva, 2013). We aimed at differences in the source concept in different languages, as well as in different means for expressing the comparison (simile, superlatives, compounds etc.). Our research relies on four bilin- gual aligned corpora containing mostly literary texts and involving English, French (Stankovi´c et al., 2017), German (Andonovski et al., 2019) and Italian (Periši´c et al., 2023) as one of the lan- guages and Serbian as the other.1 Similes have the recognizable formal structure; their surface form consists of the subject of com- parison, the object of comparison, a conjunction which signals a comparison, and the basis of the comparison implied by the expression. In our pre- vious research (Mitrovi´c et al., 2020; Krstev, 2021) we collected a set of 558 similes from Serbian lit- erary texts and presented their structure in a form of finite-state automata (FST), which facilitates our present research. Each FST describes possible lexico-syntactic variants of a simile. In the present study we focus on similes and their translations. Similes in Serbian texts were retrieved with the high precision (close to 100%) using the set of FSTs. We estimate that the re- call is significantly lower, but the goal of this re- search was not to retrieve all similes in analysed texts. Similes in other languages were retrieved using CQL (corpus query language) incorporated in systems that support aligned corpora. The cor- respondence between similes in one language and its source or translation in another language were established by using the “close reading” technique. 1English/Serbian (4.4MW) and French/Serbian (1.7MW) are available at Korpus, German/Serbian (1.6MW) and Ital- ian/Serbian (1MW) at Biblisha, and English/Serbian and Ital- ian/Serbian at Noske as well. These corpora are available to registered users. 2 Similarities Across Languages We established previously that the most frequent simile in Serbian literary texts is beo kao sneg ‘white as snow’ (Krstev, 2021:126). Therefore it is not surprising that this figure occurs in all our corpora. 1984-en: ...with everything forgiven, his soul white as snow; 1984-fr: Tout était pardonné et son âme était blanche comme neige; 1984-sr: ...gde mu je sve bilo oprošteno, gde mu je duša bila bela kao sneg; Eco-it: Chi aveva parlato era un monaco curvo per il peso degli anni, bianco come la neve; Eco-sr: Te je reˇci izgovorio monah poguren pod teretom godina, beo kao sneg; Jelinek-de: ...diese lang vergessene Weiblichkeit mit der Haut so weiß wie Schnee und dem Haar so schwarz wie Ebenholz. Jelinek-sr: ...ta dugo zaboravljena ženstvenost sa kožom belom kao sneg i kosom crnom kao abonos. Although the popularity of beo kao sneg ‘white as snow’ in Serbian was already established, in our aligned corpora it occurred only in translations to Serbian. Therefore, in all presented examples the phrase was used in the original; only 1984-fr is the translation from English. Today it is still natural to compare the pure white color to snow. In some cases, like zdrav kao dren ‘healthy as dogwood’, the motivation for using this particular tree for a comparison is blurred. In the French original and Italian translation different choices were observed: chêne ‘oak’ in French and pesce ‘fish’ in Italian. Flaubert-fr: Quant à lui il se portait toujours comme un chêne; Flaubert-sr: Što se njega tiˇce, on je zdrav kao dren; Andri´c-sr: ...i svak