Titolo

IJCoL - Italian Journal of Computational Linguistics

IJCOL - Italian Journal of Computational Linguistics Volume 11, Number 2 december 2025 Special Issue Bridging Theoretical Linguistics and Automated Language Processing: Emerging Synergies and Advances
vol. 11, n. 2

a cura di Alessandro Lenci, Marco Passarotti, Rachele Sprugnoli, Fabio Tamburini

anno di pubblicazione 2025
pp. 140
ISBN pdf 9791255001522

In recent years, Natural Language Processing (NLP) has gone through a radical breakthrough due to the success of Large Language Models (LLMs). These models have demonstrated that an unsupervised inductive approach, based on processing large amounts of raw data, can achieve extraordinary accuracy rates in many NLP-related tasks. This progress seems to have reduced the role of theoretical linguistics in computational approaches to linguistic analysis. For instance, metalinguistic annotation, a central element in supervised learning, which traditionally relies on established linguistic theories, has lost its core place in developing trained models for different NLP tasks.
In this context, a closer dialogue between theoretical and computational linguistics becomes essential to integrate theoretical and practical perspectives, bridging the gap between these two approaches to language study. This is particularly important for both the practical implementation of linguistic theories in computational models and the theoretical interpretation of results produced by NLP systems. This special issue of the Italian Journal of Computational Linguistics aims to bridge this gap by exploring the emerging synergies between theoretical linguistics and computational linguistics and the implications these have for the future of the two approaches.

bio indice articoli

Alessandro Lenci, Università di Pisa

Marco Passarotti, Università Cattolica del Sacro Cuore

Rachele Sprugnoli, Università Cattolica del Sacro Cuore

Fabio Tamburini, Università di Bologna

Preface to the Special Issue Bridging Theoretical Linguistics and Automated Language Processing: Emerging Synergies and Advances
Alessandro Lenci, Marco Passarotti, Rachele Sprugnoli, Fabio Tamburini

Bridging Linguistics and Computational Linguistics: Insights into Synergies and Challenges from a Case Study
Simonetta Montemagni

Theoretical Implications of Automated Discourse Parsing in Student Writing
Arianna Bienati, Mariachiara Pascucci, Jennifer-Carmen Frey, Alessio Palmero Aprosio

The Development of a Medical Dataset in Italian Sign Language (LIS): Theoretical Considerations and Practical Applications
Gaia Caligiore

Large Language Models Under Evaluation: An Acceptability, Complexity and Coherence Assessment in Italian
Cristiano Chesi, Francesco Vespignani, Roberto Zamparelli

The Pragmatic Utility of Asking the Right Question in a Recommendation Scenario
Martina Di Bratto, Maria Di Maro

Italian-based Large Language Models at the Syntax-Semantics Interface: the Case of Instrumental Role
Alice Suozzi, Simone Mazzoli, Gianluca E. Lebani

Lenci - Passarotti - Sprugnoli - Tamburini, Preface to the Special Issue Bridging Theoretical Linguistics and Automated Language Processing: Emerging Synergies and Advances
	pdf 77.3 KB	DOI 10.17454/IJCOL112.01	IJCoL_11_2_01	0,00 €	IJCoL_11_2_01"
Montemagni, Bridging Linguistics and Computational Linguistics: Insights into Synergies and Challenges from a Case Study
	pdf 456.2 KB ABSTRACT ↗ This paper outlines the evolving interplay between Linguistics and Computational Linguistics, aiming to map the current state of their interactions and to identify areas where deeper integration could drive significant advancements in both areas. Since the early days of Computational Linguistics as an autonomous discipline, the synergy has developed in parallel with progress in both computational methods and linguistic theory. Computational modeling of language offers a powerful framework to investigate core questions of linguistics, from how language works and is acquired, to how it changes across time, space, communicative situations, and domains. Despite this potential, the capabilities of state-of-the-art computational methods remain only partially exploited within linguistic research, leaving a gap between advances in Natural Language Processing and the needs of linguistics. This paper seeks to examine the current landscape of this synergy, its scientific and practical implications, and the challenges that must be addressed to fully harness its potential. A pilot study is presented to illustrate how linguistic resources and computational modeling can provide answers to long-standing research questions and, at the same time, open up new avenues for investigating open issues in language typology.	DOI 10.17454/IJCOL112.02	IJCoL_11_2_02	0,00 €	IJCoL_11_2_02"
Bienati - Pascucci - Frey - Palmero Aprosio, Theoretical Implications of Automated Discourse Parsing in StudentWriting
	pdf 2.9 MB ABSTRACT ↗ This article presents a study on annotating explicit discourse relations in Italian student essays, comparing human annotations with outputs from generative large language models and examining their alignment with theoretical models of textuality. We review prior work on automatic discourse relation annotation in Italian, highlighting limitations in language coverage, especially in out-of-domain scenarios and how these have been addressed. Our experiments explore the use of generative models to mitigate the scarcity of domain-specific training data, while assessing their ability to reflect the intended theoretical framework. We evaluate two generative models in detecting connectives and classifying their senses, comparing results to human annotation. For our evaluation sample, we use a string-matching algorithm combined with a rule-based approach to pre-annotate essays with possible connective forms and their senses, based on their presence in the Lexicon of Italian Connectives (LICO). These annotations were manually corrected by two expert annotators, resulting in a publicly available evaluation sample. The study raises significant theoretical questions about the definition of connectives, its relationship to text segmentation and the challenges both human and machines face when annotating discourse relations. Our findings show how computational approaches can shed light on linguistic theories and, vice versa, how linguistic theories can guide the application of computational resources.	DOI 10.17454/IJCOL112.03	IJCoL_11_2_03	0,00 €	IJCoL_11_2_03"
Cagliore, The Development of a Medical Dataset in Italian Sign Language (LIS): Theoretical Considerations and Practical Applications
	pdf 1.8 MB ABSTRACT ↗ Automatic translation to and from Italian Sign Language (LIS) requires the development of computational models, such as avatars, capable of accurately reproducing both manual and non-manual articulators of signed discourse. This, in turn, demands the creation of machineprocessable and linguistically robust data collections, built through the segmentation, transcription and systematic categorization of signs, to capture their internal structure and relational dynamics. Such a framework should reflect the multilinear organization of LIS, which poses several challenges. These include the visual-gestural and simultaneous nature of LIS, the absence of a standardized written form and the scarcity of available resources. Key challenges arise at multiple levels, including the very development of LIS resources, the identification of suitable tools for capturing signed data and the lack of a standardized coding system for signed languages. These aspects were addressed in the development of the MultiMedaLIS Dataset (MULTImodal MEDicAl LIS Dataset), a preliminary dataset in the medical domain, collected using multimodal capturing tools. Annotation, performed using ELAN, followed the principles of simplicity and readability by employing multilayered labelling in both Italian and English, along with a dedicated annotation system for signed languages. In this way, the Dataset is accessible to both signers and non-signers, currently serving as a resource for linguistic analyses, as well as for training algorithms for automatic sign recognition.	DOI 10.17454/IJCOL112.04	IJCoL_11_2_04	0,00 €	IJCoL_11_2_04"
Chesi - Vespignani - Zamparelli, Large Language Models Under Evaluation: An Acceptability, Complexity And Coherence Assessment In Italian
	pdf 1.2 MB ABSTRACT ↗ This paper discusses the results of various experiments assessing the morphosyntactic and semantic competence in Italian of four very large language models (vLLMs): davinci (GPT-3/ChatGPT), davinci-002, davinci-003 (both GPT-3.5 models) and gpt-4-1106-preview (GPT-4).We evaluated these models on (i) acceptability, (ii) complexity, and (iii) coherence judgments using 7-point Likert scales and on (iv) syntactic development through a forced choice task. The test sets were drawn from shared NLP tasks and standard linguistic assessments. The results suggest that, although fine-tuned transformers outperform all GPT models, GPT-4 represents a significant improvement over third-generation GPT models. According to our tests, even if GPT-4 and fine-tuned transformers cannot be considered descriptively or explanatorily adequate, they nonetheless pose a challenge to the poverty of the stimulus hypothesis. The "theory" expressed by GPT models is not linguistically intelligible in any relevant sense, and their training data is orders of magnitude larger than the primary linguistic input available to children. Nevertheless, GPT-4 captures certain generalizations, such as the constraints blocking the insertion of an overt resumptive clitic in specific gap positions, that are arguably unlearnable from just primary positive data.	DOI 10.17454/IJCOL112.05	IJCoL_11_2_05	0,00 €	IJCoL_11_2_05"
Di Bratto - Di Maro
	pdf 771.9 KB ABSTRACT ↗ The formal study of argumentation-based dialogue lacks a comprehensive reference framework, particularly from a linguistic perspective. This work addresses part of this gap by analysing whether the relationship between the semantic-syntactic structure of questions and their pragmatic features could increase the utility of the corresponding answers. A preliminary experiment was carried out to identify which forms of information-seeking request best prompt useful human responses, thereby efficiently solving decision problems. Four question types were tested alongside alternative formulations to examine the influence of pragmatic features on answer quality and cognitive effort. Results suggest that pragmatic features can affect the informativeness of answers, with polar questions tending to elicit informative or over-informative responses, while content questions displayed greater variability. Additionally, questions containing polarity items appeared to increase cognitive load, as reflected in response patterns. While these findings are necessarily tentative due to the exploratory nature of the study, they offer promising indications for the design of linguistically grounded, argumentation-based dialogue systems and point to several avenues for further research, including broader experimental designs and an expanded set of pragmatic variables.	DOI 10.17454/IJCOL112.06	IJCoL_11_2_06	0,00 €	IJCoL_11_2_06"
Italian-based Large Language Models at the Syntax-Semantics Interface: the Case of Instrumental Role
	pdf 561.1 KB ABSTRACT ↗ The linguistic competence of Large Language Models (LLMs) has been the focus of extensive investigation in recent years. Yet, the syntax-semantics interface remains a relatively understudied aspect of LLMs’ linguistic abilities. This study aims to address this gap by focusing on the Instrumental role in Italian. In this language, Instruments can always be syntactically omitted, yet they remain semantically present, as they are recoverable either from the verb meaning alone (when the verb is presented in isolation) or from the interaction between the verb meaning and that of its internal argument (when the verb appears within a syntactic context). To assess the ability of LLMs to semantically determine the most appropriate Instrument(s) from the verb meaning, we conducted two experiments based on psycholinguistically inspired tasks, comparing the performance of GePpeTto and Minerva models (350M, 1B, 3B and 7B) to that of Italian speakers. In the first experiment, verbs were presented in isolation, while in the second, they were presented within a syntactic context. Our findings indicate that the performance of LLMs is influenced by the semantic selectivity of verbs, the presence or absence of a clausal context and model characteristics.	DOI 10.17454/IJCOL112.07	IJCoL_11_2_07	0,00 €	IJCoL_11_2_07"

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