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 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.