The aim of the paper is to present a first attempt at annotating Information Structure roles in syntactic treebanks of the Universal Dependencies collection, discussing theoretical considerations and practical methodological questions while presenting our core annotation principles. We focus on constructions in which Topic or Focus is overtly marked through (morpho)syntactic means. The proposed annotation is illustrated using examples from five languages: Wolof, Japanese, Tundra Nenets, Hungarian, and Italian.

Historical parliamentary debates are essential for longitudinal political and linguistic research, yet much early material remains available only as scanned images. In the Italian context, proceedings from 1848–1996 lack large-scale, structurally annotated, machine-readable representations. This paper addresses the challenge of transforming historical Italian parliamentary debates into structured corpora by moving beyond plain Optical Character Recognition (OCR) toward functional block segmentation and speaker attribution. We present detailed annotation guidelines and a manually annotated dataset of 300 randomly sampled pages. Two approaches are compared: (i) direct multimodal Large Language Model (LLM) annotation and (ii) a modular pipeline combining OCR with LLM-based structural reconstruction under zero-shot and few-shot prompting. Evaluation on a held-out test set shows that separating transcription from structural reasoning improves performance, with few-shot prompting yielding the most reliable results. The study demonstrates the feasibility of integrating LLM-based reasoning into historical parliamentary digitisation workflows.

This paper addresses the representation of ellipsis in dependency syntax, proposing both a theoretical and a practical workflow for its analysis and annotation in treebanks, following the state-of-the-art Universal Dependencies framework. We discuss the challenges of annotating ellipsis, with a focus on predicate ellipsis and its representation in dependency treebanks, and emphasize the importance of accounting for such phenomena for syntactic analysis and machine learning applications. We present a case study based on the Italian-Old treebank, demonstrating the applicability of the proposed workflows and invite the community to participate in this initiative with their own languages.

In this paper, we conduct parsing experiments on Dante Alighieri’s Divine Comedy, an Old Italian poem composed between 1306-1321 and organized into three Cantiche —Inferno, Purgatorio, and Paradiso. We perform parsing on subsets of the poem using both a Modern Italian training set and sections of the Divine Comedy itself to evaluate under which scenarios parsers achieve higher scores. We find that employing in-domain training data supports better results, leading to an increase of approximately +17% in Unlabeled Attachment Score (UAS) and +25-30% in Labeled Attachment Score (LAS). Subsequently, we provide brief commentary on the differences in scores achieved among subsections of Cantiche, and we conduct experimental parsing on a text from the same period and style as the Divine Comedy.

In this paper, we create and evaluate non-combined and combined models using Old and Contemporary Italian data to determine whether increasing the size of the training data with a combined model could improve parsing accuracy to facilitate manual annotation. We find that, despite the increased size of the training data, in-domain parsing performs better. Additionally, we discover that models trained on Old Italian data perform better on Contemporary Italian data than the reverse. We attempt to explain this result in terms of syntactic complexity, finding that Old Italian text exhibits higher sentence length and non-projectivity rate.