A E uropean P ortuguese corpus annotated for verbal idioms (2025)

Abstract: This paper presents the construction of VIDiom-PT, a corpus in European Portuguese annotated for verbal idioms (e.g. O Rui bateu a bota, lit.: Rui hit the boot ‘Rui died’). This linguistic resource aims to support the development of systems capable of processing such constructions in this language variety. To assist in the annotation effort, two tools were built. The first allows for the detection of possible instances of verbal idioms in texts, while the second provides a graphical interface for annotating them. This effort culminated in the annotation of a total of 5,178 instances of 747 different verbal idioms in more than 200,000 sentences in European Portuguese. A highly reliable inter-annotator agreement was achieved, using Krippendorff’s alpha for nominal data (0.869) with 5% of the data independently annotated by 3 experts. Part of the annotated corpus is also made publicly available.

## introduction this paper addresses verbal idioms (or idiomatic expressions), with a focus on european portuguese (pt). these are a special type of multiword expression (mwe) where the main verb and one or more of its arguments are frozen together @xcite , that is, they have unpredictable distributional and syntactic constraints. furthermore, the overall meaning of these expressions often cannot be derived from the meaning that each element presents when used separately; in other words, the meaning of these constructions is non-compositional @xcite . the example 'a ana atirou o projeto às urtigas' (lit. "ana threw the project at the nettles") "ana abandoned the project" showcases how the conventionalized meaning conveyed by these expressions cannot be directly deduced from its constituents. several aspects make analyzing and automatically processing these expressions a challenging task, notably, the unpredictability of distributional constraints; the limited possibility of inflection of frozen complements; the syntactic structure they often exhibit, allowing for insertions and permutations of constituents; and the non-compositionality of these expressions; in addition, their frequency in texts is usually very low. although one might assume that the frequency of mwes in spoken dialogue or written text is low enough to disregard their unique characteristics during text analysis, their estimated number in a native speaker's lexicon is surprisingly significant. estimates range from being of the same order of magnitude as the number of single-word verbs @xcite ; to several times the number of simple, distributional verbs: for example, @xcite presents a french lexicon of 20,340 frozen sentences, which contrasts with that of 13,225 simple, distributionally free, verbs. considering all of this, it is clear that to achieve a good performance in the syntactic and semantic analysis of natural language texts, one cannot overlook the existence of these constructions, as they contain essential information to understand the content of a given text. moreover, studies have shown how properly identifying mwe can lead to better parser performance @xcite as it reduces parsing errors. a great amount of work has been developed to integrate the analysis of verbal idioms into nlp systems (de uzeda @xcite @xcite . as posited by @xcite , several natural language processing (nlp) systems address mwes by resorting to a lexicon. this is also the case for the system used in this paper (self-reference), which also adopts a lexicon-based approach, in this case, a lexicon of verbal idioms. in this system, this lexicon takes the form of a lexicon-grammar, that is, a matrix database, where lines correspond to the lexical entries (the verbal idioms) and columns encode their structural, distributional, semantic, and transformational properties. at the time of writing, the lexicon-grammar of verbal idioms of european portuguese contains 2,714 lexical entries and 106 columns describing their individual properties. it is based on this lexicon-grammar and the linguistic constraints described therein that the system identifies instances of verbal idioms, through the extraction of a relation called fixed, linking the frozen elements of the verbal idiom (e.g., 'meter a mão na massa' (lit. "to put the hand in the dough") "to work actively on something" which is represented by fixed_c1p2(meter,mão,na,massa)). in order to assess the ability of such systems at identifying natural occurrences of verbal idioms, it is essential to have access to written texts (corpora) annotated with this phenomenon. recently, the parseme project @xcite foot_0 , an initiative developed by a european research network focused on the role of mwe in parsing, produced a multilingual 5-million-word annotated corpus. this includes a brazilian portuguese partition, which served as the basis for a mwe identification shared task @xcite . for verbal idioms specifically, the second edition of this shared task @xcite found that around 20% of the annotated mwe (1,130 out of 5,536) corresponded to verbal idioms (tagged as 'vid'). it is important to note that no equivalent corpus in the european variety had been included in any edition of this shared task, and that, while the two varieties are quite similar and intercomprehensible most of the time, a previous comparison experiment @xcite has shown that they only share a reduced number of equivalent verbal idioms (around 10%). it was, therefore, essential to create a new corpus for european portuguese, since, to the best of our knowledge, no such resource, if it exists, has been made publicly available until now. ## related work the annotation of idioms in english corpora has seen a significant amount of work. one can find important resources like the 'high fixed corpus' and 'low fixed corpus' presented in @xcite . this project aimed to advance the machine translation of verbal idioms employing a substitution method and using 3 dictionaries: a dictio-nary of idioms in the source language; a dictionary of idioms in the target language; and a bilingual dictionary with a correspondence between idioms of the two languages. to test their system, they chose to translate between english and brazilian-portuguese and built two test corpora: the 'high fixed corpus' and 'low fixed corpus'. the first corpus features 17 different idioms of the type verb + noun, while the second one features 11 different idioms of the same type. these corpora contain 10 sentences featuring each different idiom which were extracted from the web. in more recent years, there are works like @xcite , which focuses on the automatic identification of potential idiomatic expressions based on existing dictionaries of idioms. potential instances of such constructions are extracted from the british national corpus (bnc), through a parsing-based method that considers the lemmata and the dependency relations. they are then manually annotated using graphical interfaces built for that purpose. the sense of these idioms is classified, mainly as being literal or non-literal. @xcite culminated in the magpie corpus which features 56,622 annotated phrases with 1,756 different idiom types annotated as being literal or not. adewumi et al. (2021) performed a similar task with two main differences: the extraction of potential idiomatic expressions was performed manually, which reduces the likelihood of false-positives and false-negatives, but massively increases the amount of time and effort required for this task; the annotation of idioms considered a broader set of senses such as 'irony' and 'euphemism'. this project achieved a corpus with 1,197 cases of idioms totaling over 20,100 samples/sentences. when it comes to other languages, @xcite is a good example of a similar approach to verbal idiom annotation. first, they use a dependency parser for japanese and a dictionary of japanese idioms to detect examples of these expressions in the japanese web corpus @xcite . then, human annotators classify the expressions as idiomatic or literal, which resulted in a corpus spanning 146 ambiguous idioms across 102,846 sentences. recently, for german, ehren et al. (2024) presented another effort towards the annotation of verbal idioms. based on an electronic dictionary of german idioms (featuring roughly 30,000 verbal idioms), candidate instances of relevant expressions are fetched from the parallel meaning bank (pmb), using the same extraction method described in @xcite . potential idiomatic expressions are marked as one of 5 categories: idiomatic, probably idiomatic, probably literal, literal or both, which poses an interesting variation from the rest of the works here discussed, as it addresses the lack of context that is made available to the annotator. the resulting collection features 1,945 annotated verbal idioms across 5,821 sampled sentences. for the target language of this article, european portuguese, the amount of work addressing verbal idioms is scarce. however, the mwe research topic in general has seen the construction of resources, namely, a lexical database of mwes of portuguese in the scope of project combina-pt @xcite . the expressions were automatically extracted through the analysis of a balanced 50 million word written corpus sampled from the reference corpus of contemporary portuguese (in portuguese, corpus de referência do português contemporâneo). this information was then statistically interpreted with lexical association measures and validated by hand. the phenomena were broadly classified as 5 types of mwe: (i) groups forming a lexical category, (ii) groups forming a phrase (e.g., nominal or adverbial phrase), (iii) groups that constitute a verbal phrase (the group of which verbal idioms are a part of), (iv) groups that specify named entities, and (v) cases that require further attention as they are doubtful expressions (includes some verbal idioms). lastly, one can find works like lidioms @xcite which consists of a multilingual dataset of idioms (in general) containing five languages: english, german, italian, portuguese, and russian. the data was crawled and integrated from 4 online data sources. the idioms had to be manually filtered by experts, so that only the non-compositional constructions (corresponding to roughly half of the crawled expressions) were considered. moreover, all idioms were evaluated by two native speakers and one linguist (per language) in order to ensure the quality of the data. the lidioms dataset provides linking between idioms across languages by using english as a pivot language since all the target translations are in english. this means multilingual translation makes use of inference and multiple bilingual patterns, where english definitions are used as a bridge. this dataset presents a total of 13,889 annotated samples which model 815 different concepts with 488 translations (where 115 are indirect translations). ## results it is noteworthy that the documents from the portuguese parliament exhibit a substantially higher number of potential verbal idioms compared to the other document source. while the presence of potential verbal idioms does not directly reflect the frequency of valid idiomatic instances, in this case, the number of annotated verbal idioms is also significantly greater in the parliamentary documents. taking this analysis further, we observe that the verbal idioms annotated in the documents from this source exhibit considerably less variation, with a total of 377 distinct expressions, compared to the 606 different constructions identified in the cetem-público documents (resulting in an overall count of 747 distinct verbal idioms). this suggests that the higher number of verbal idioms in the first source is primarily driven by the repetition of the same, likely context-specific, constructions. this hypothesis is reinforced by expressions such as 'esgotar o tempo' (lit. "to deplete the time") "to run out of time" and 'usar da palavra' (lit. "to use of the word") "to speak", which appear frequently in the portuguese parliament documents, with 275 and 128 instances, respectively, whereas in the other source, they occur only three times each. it is important to highlight that approximately 50% of the detected potential verbal idioms correspond to actual idiomatic expressions. this finding suggests that the criteria established for identifying potential verbal idioms are sufficiently stringent to prevent an excessive number of non-idiomatic constructions from being captured. when it comes to the number of frozen elements in the annotated verbal idioms, lastly, it is noteworthy that the lexicon-grammar matrix describes a total of 2,714 different verbal idioms, of which only 747 were actually found in the documents analyzed. this makes evident how rare some of these idiomatic constructions really are, as well as the relevance of building and maintaining lexicons of such mwe. considering that recent trends in nlp consist of training models on existing data/texts, the sparse distribution of verbal idioms in corpora may raise concerns regarding the overall efficacy of these approaches instead of lexicon-based methods @xcite . ## conclusion this paper introduced vidiom-pt, a corpus of european portuguese annotated for verbal idioms which is made publicly available. we outlined the selection criteria for source texts, the lexicongrammar framework adopted for the linguistic description of verbal idioms, the annotation process-including guidelines-and the development of two annotation tools, culminating in a fully annotated dataset. the paper discusses several issues involved in the annotation process, mostly the challenge of distinguishing idiomatic (i.e., noncompositional) from literal meanings, a central issue in idiom annotation. the resulting corpus comprises 5,178 annotated instances covering 747 distinct verbal idioms. the annotation process was validated through an inter-annotator agreement assessment, yielding a krippendorff's alpha of 0.869 based on independent annotations of 5% of the data by three specialists, indicating a high level of reliability. a golden standard was established based on the consensus annotation of this data subset. we anticipate that vidiom-pt will serve as a valuable resource for advancing research in various nlp tasks involving verbal idioms in european portuguese, including idiom identification, meaning extraction, and machine translation.