Designing vocabulary learning activities for foreign/second language (L2) learners highly depends on the successful identification of difficult words. In this paper, we present a novel personalised word difficulty classifier for L2 Spanish, using the LexComSpaL2 corpus as training data and a BiLSTM model as the architecture. We train a base version (using the original LexComSpaL2 data) and a word family version of the classifier (adding word family knowledge as an extra feature). The base version obtains reasonably good performance (F1 = 0.53) and shows weak positive predictive power (φ = 0.32), underlining the potential of automated methods in determining vocabulary difficulty for individual L2 learners. The “word family classifier” is able to further push performance (F1 = 0.62 and φ = 0.45), highlighting the value of well-chosen linguistic features in developing word difficulty classifiers.

Dependency parsing of non-normative language varieties remains a challenge for modern NLP. While contemporary parsers excel at standardized languages, dialectal variation – especially in function words, conjunctives, and verb clustering – introduces syntactic ambiguity that disrupts traditional parsing approaches. In this paper, we conduct a quantitative evaluation of syntactic dependencies in Southern Dutch dialects, leveraging a standardized dialect corpus to isolate syntactic effects from lexical variation. Using a neural biaffine dependency parser with various mono- and multilingual transformer-based encoders, we benchmark parsing performance on standard Dutch, dialectal data, and mixed training sets. Our results demonstrate that incorporating dialect-specific data significantly enhances parsing accuracy, yet certain syntactic structures remain difficult to resolve, even with dedicated adaptation. These findings highlight the need for more nuanced parsing strategies and improved syntactic modeling for non-normative language varieties.

We present LexComSpaL2, a novel corpus which can be employed to train personalised word-level difficulty classifiers for learners of Spanish as a foreign/second language (L2). The dataset contains 2,240 in-context target words with the corresponding difficulty judgements of 26 Dutch-speaking students who are learning Spanish as an L2, resulting in a total of 58,240 annotations. The target words are divided over 200 sentences from 4 different domains (economics, health, law, and migration) and have been selected based on their suitability to be included in L2 learning materials. As our annotation scheme, we use a customised version of the 5-point lexical complexity prediction scale (Shardlow et al., 2020), tailored to the vocabulary knowledge continuum (which ranges from no knowledge over receptive mastery to productive mastery; Schmitt, 2019). With LexComSpaL2, we aim to address the lack of relevant data for multi-category difficult prediction at word level for L2 learners of other languages than English.

This study presents a lexical simplification (LS) methodology for foreign language (FL) learning purposes, a barely explored area of automatic text simplification (TS). The method, targeted at Spanish as a foreign language (SFL), includes a customised complex word identification (CWI) classifier and generates substitutions based on masked language modelling. Performance is calculated on a custom dataset by means of a new, pedagogically-oriented evaluation. With 43% of the top simplifications being found suitable, the method shows potential for simplifying sentences to be used in FL learning activities. The evaluation also suggests that, though still crucial, meaning preservation is not always a prerequisite for successful LS. To arrive at grammatically correct and more idiomatic simplifications, future research could study the integration of association measures based on co-occurrence data.