This paper addresses challenges encountered in constructing lexical databases, specifically WordNets, for three ancient Indo-European languages: Ancient Greek, Latin, and Sanskrit. The difficulties partly arise from adapting concepts and methodologies designed for modern languages to the construction of lexical resources for ancient ones. A further significant challenge arises from the goal of creating WordNets that not only adhere to a neo-structuralist relational view of meaning but also integrate Cognitive Semantics concepts, aiming for a more realistic representation of meaning. This integration is crucial for facilitating studies in diachronic semantics and lexicology, and representing meaning in such a nuanced manner becomes paramount when constructing language resources for theoretical research, rather than for applied tasks, as is the case with lexical resources for ancient languages. The paper delves into these challenges through a case study focused on the TEMPERATURE conceptual domain in the three languages. It outlines difficulties in distinguishing prototypical and non-prototypical senses, literal and non-literal ones, and, within non-literal meanings, between metaphorical and metonymic ones. Solutions adopted to address these challenges are presented, highlighting the necessity of achieving maximum granularity in meaning representation while maintaining a sustainable workflow for annotators.

The paper introduces [DATASET], a resource that builds on the ValPaL database of verbs’ valency patterns and alternations by adding a number of ancient languages (completely absent from ValPaL) and a number of new features that enable direct comparison, both diachronic and synchronic. For each verb, ValPaL contains the basic frame and ideally all possible valency alternations allowed by the verb (e.g. passive, causative, reflexive etc.). In order to enable comparison among alternations, an additional level has been added, the alternation class, that overcomes the issue of comparing language specific alternations which were added by individual contributors of ValPaL. The ValPaL had as its main aim typological comparison, and data collection was variously carried out using questionnaires, secondary sources and largely drawing on native speakers’ intuition by contributors. Working with ancient languages entails a methodological change, as the data is extracted from corpora. This has led to re-thinking the notion of valency as a usage-based feature of verbs and to planning future addition of corpus data to modern languages in the database. It further shows the impact of ancient languages on theoretical reflection.

The Sanskrit WordNet is a resource currently under development, whose core was induced from a Vedic text sample semantically annotated by means of an ontology mapped on the Princeton WordNet synsets. Building on a previous case study on Ancient Greek (Zanchi et al. 2021), we show how sentence frames can be extracted from morphosyntactically parsed corpora by linking an existing dependency treebank of Vedic Sanskrit to verbal synsets in the Sanskrit WordNet. Our case study focuses on two verbs of asking, yāc- and prach-, featuring a high degree of variability in sentence frames. Treebanks enhanced with WordNet-based semantic information revealed to be of crucial help in motivating sentence frame alternations.

This paper shows how WordNets can be employed in tandem with morpho-syntactically annotated corpora to study poetic formulas. Pairing the lexico-semantic information of the Sanskrit WordNet with morpho-syntactic annotation from the Vedic Treebank, we perform a pilot study of formulas including SPEECH verbs in the RigVeda, the most ancient text of the. Sanskrit literature.

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE’s development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.

This paper describes an ongoing endeavor to construct Pavia Verbs Database (PaVeDa) – an open-access typological resource that builds upon previous work on verb argument structure, in particular the Valency Patterns Leipzig (ValPaL) project (Hartmann et al., 2013). The PaVeDa database features four major innovations as compared to the ValPaL database: (i) it includes data from ancient languages enabling diachronic research; (ii) it expands the language sample to language families that are not represented in the ValPaL; (iii) it is linked to external corpora that are used as sources of usage-based examples of stored patterns; (iv) it introduces a new cross-linguistic layer of annotation for valency patterns which allows for contrastive data visualization.

Indo-European preverbs are uninflected morphemes attaching to verbs and modifying their meaning. In Early Vedic and Homeric Greek, these morphemes held ambiguous morphosyntactic status raising issues for syntactic annotation. This paper focuses on the annotation of preverbs in so-called “absolute” position in two Universal Dependencies treebanks. This issue is related to the broader topic of how to annotate ellipsis in Universal Dependencies. After discussing some of the current annotations, we propose a new scheme that better accounts for the variety of absolute constructions.

Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this has social consequences: how do linguistic expressions of gender-based violence (GBV) influence who we perceive as responsible? We build on previous psycholinguistic research in this area and conduct a large-scale perception survey of GBV descriptions automatically extracted from a corpus of Italian newspapers. We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows solid overall performance, with large differences between dimensions and participants: salient _focus_ is more predictable than salient _blame_, and perpetrators’ salience is more predictable than victims’ salience. Experiments with ridge regression models using different representations show that features based on linguistic theory similarly to word-based features. Overall, we show that different linguistic choices do trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. This work can be a core instrument to raise awareness of the consequences of different perspectivizations in the general public and in news producers alike.

This paper presents the work in progress toward the creation of a family of WordNets for Sanskrit, Ancient Greek, and Latin. Building on previous attempts in the field, we elaborate these efforts bridging together WordNet relational semantics with theories of meaning from Cognitive Linguistics. We discuss some of the innovations we have introduced to the WordNet architecture, to better capture the polysemy of words, as well as Indo-European language family-specific features. We conclude the paper framing our work within the larger picture of resources available for ancient languages and showing that WordNet-backed search tools have the potential to re-define the kinds of questions that can be asked of ancient language corpora.