This paper introduces MultiGEC, a dataset for multilingual Grammatical Error Correction (GEC) in twelve European languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Russian, Slovene, Swedish and Ukrainian. MultiGEC distinguishes itself from previous GEC datasets in that it covers several underrepresented languages, which we argue should be included in resources used to train models for Natural Language Processing tasks which, as GEC itself, have implications for Learner Corpus Research and Second Language Acquisition. Aside from multilingualism, the novelty of the MultiGEC dataset is that it consists of full texts — typically learner essays — rather than individual sentences, making it possible to train systems that take a broader context into account. The dataset was built for MultiGEC-2025, the first shared task in multilingual text-level GEC, but it remains accessible after its competitive phase, serving as a resource to train new error correction systems and perform cross-lingual GEC studies.

In this paper, we argue that discourse representations can be mapped to networks and analyzed by tools provided in network theory so that deep properties of discourse structure are revealed. Two discourse-annotated corpora, C58 and STAC, that belong to different discourse types and languages were compared and analyzed. Various key network indices were used for the discourse representations of both corpora and show the different network profiles of the two discourse types. Moreover, both network motifs and antimotifs were discovered for the discourse networks in the two corpora that shed light on strong tendencies in building or avoiding to build discourse relations between utterances for permissible three-node discourse subgraphs. These results may lead to new types of discourse structure rules that draw on the properties of the networks that lie behind discourse representation. Another important aspect is that the second version of the STAC corpus, which includes nonlinguistic discourse units and their relations, exhibits similar trends in terms of network subgraphs compared to its first version. This suggests that the nonlinguistic context has a significant impact on discourse structure.

This study investigates the acquisition of grammatical gender in Russian learners of Greek. Agreement in Determiner-Noun (Det-N) and Adjective-Noun (Adj-N) dependencies is explored through eye-tracking registration in a reading-based design. Twenty-four intermediate learners read short texts embedded with agreement violations, and then responded to a comprehension task. The study implemented a two-level triangulation by drawing its stimuli from the Greek Learner Corpus II (GLCII) and contrasting, at a first level, the findings with comparable offline data that were previously obtained from the same corpus. The second level entailed a contrast between the online evidence and the offline data that were collected through a post-reading questionnaire right after the online eye-tracking session. This questionnaire explored whether longer fixations on agreement violations are associated with explicit awareness of the study’s focus. To anticipate the outcome of the study, the gender agreement data suggests that the abstract gender feature is present in the developing grammar of Russian learners of Greek. Moreover, the participants seem to effectively deal with the syntactic computations underlying nominal agreement, though efficacy varies across the structures that have been examined. Apart from this, certain suggestions are made considering the research paradigm followed.

By means of an annotated corpus of 13,085 words, this paper investigates discourse-level anaphora in L1 Greek, exploring the anaphoric preferences of referring expressions in subject position and the effect of several factors on antecedent prominence. Longer referential forms are supposed to signal low accessibility, whereas shorter ones tend to be linked to more salient discourse elements. Moreover, antecedent saliency appears to be influenced by syntactic position, sentential/discourse topichood, recency and rhetoric relations between utterances (Right Frontier Constraint). The results of the study indicate that, although the use of null pronominals is the default option in Greek, the ‘second-in-command’ anaphoric expression is the full DP and not the overt pronoun. Furthermore, null pronouns significantly prefer to refer to salient antecedents.

This article investigates the potential of using error-annotated L2 utterances and their associated language profile to more accurately assess the language proficiency of an L2 learner. The main goal of this paper is to demonstrate the use of machine learning methods for uncovering features of the learner's interlanguage and their proficiency level, as well as to answer three questions: a) is it feasible to use error-annotated data along with linguistic profile metadata to trace patterns related to aspects of learners’ interlanguage? b) How important may error annotation be in estimating the learner’s proficiency level? c) Which of the (extra-)linguistic profile metadata stand out in contributing to the correct classification of the proficiency level? GLCII, the largest online freely available L2-Greek corpus, is utilized as an example corpus to illustrate the approach proposed in this paper with which for any L2, central questions can potentially be addressed by the relevant machine learning method so that, in a second stage, useful conclusion can be drawn. As a final point, we urge the implementation of ML techniques as a way of expediting research in a variety of related areas in applied linguistics.