Potentially idiomatic expressions (PIEs) carry meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows evaluation of language model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.

As part of the project ParlaMint II, a new corpus of the sessions of the Portuguese Parliament from 2015 to 2022 has been compiled, encoded and annotated following the ParlaMint guidelines. We report on the contents of the corpus and on the specific nature of the political settings in Portugal during the time period covered. Two subcorpora were designed that would enable comparisons of the political speeches between pre and post covid-19 pandemic. We discuss the pipeline applied to download the original texts, ensure their preprocessing and encoding in XML, and the final step of annotation. This new resource covers a period of changes in the political system in Portugal and will be an important source of data for political and social studies. Finally, Finally, we have explored the political stance on immigration in the ParlaMint-PT corpus.

CEPLEXicon (version 1.1) is a child lexicon resulting from the automatic tagging of two child corpora: the corpus Santos (Santos, 2006; Santos et al. 2014) and the corpus Child ― Adult Interaction (Freitas et al. 2012), which integrates information from the corpus Freitas (Freitas, 1997). This lexicon includes spontaneous speech produced by seven children (1;02.00 to 3;11.12) during approximately 86h of child-adult interaction. The automatic tagging comprised the lemmatization and morphosyntactic classification of the speech produced by the seven children included in the two child corpora; the lexicon contains information pertaining to lemmas and syntactic categories as well as absolute number of occurrences and frequencies in three age intervals: < 2 years; ≥ 2 years and < 3 years; ≥ 3 years. The information included in this lexicon and the format in which it is presented enables research in different areas and allows researchers to obtain measures of lexical growth. CEPLEXicon is available through the ELRA catalogue.

We present a corpus of child and child-directed speech of European Portuguese. This corpus results from the expansion of an already existing database (Santos, 2006). It includes around 52 hours of child-adult interaction and now contains 27,595 child utterances and 70,736 adult utterances. The corpus was transcribed according to the CHILDES system (Child Language Data Exchange System) and using the CLAN software (MacWhinney, 2000). The corpus itself represents a valuable resource for the study of lexical, syntax and discourse acquisition. In this paper, we also show how we used an existing part-of-speech tagger trained on written material (Généreux, Hendrickx & Mendes, 2012) to automatically lemmatize and tag child and child-directed speech and generate a line with part-of-speech information compatible with the CLAN interface. We show that a POS-tagger trained on the analysis of written language can be exploited for the treatment of spoken material with minimal effort, with only a small number of written rules assisting the statistical model.