Word Sense Disambiguation for A ncient G reek: Sourcing a training corpus through translation alignment (2023)

Abstract: This paper seeks to leverage translations of Ancient Greek texts to enhance the performance of automatic word sense disambiguation (WSD). Satisfactory WSD in Ancient Greek is achievable, provided that the system can rely on annotated data. This study, acknowledging the challenges of manually assigning meanings to every Greek lemma, explores the strategies to derive WSD data from parallel texts using sentence and word alignment. Our results suggest that, assuming the condition of high word frequency is met, this technique permits us to automatically produce a significant volume of annotated data, although there are still significant obstacles when trying to automate this process.

## research context: towards onomasiological searches it is generally known that in natural languages there is not a one-to-one mapping between form and meaning: one form or term can express various meanings or concepts (e.g. 'bright' can refer to light or intelligence) and vice versa (e.g. there are various ways to express that a person is intelligent, including 'bright @xcite . in semantic theory, studying the various meanings that a specific form expresses is called the 'semasiological' perspective, while studying the various forms that can be used to express a certain meaning is called the 'onomasiological' perspective (see @xcite . this has important practical consequences: while it is straightforward to query most annotated corpora for specific terms, querying it for specific concepts is usually far less straightforward (see, for instance, @xcite . most corpora have not been annotated semantically, given that the annotation is labor-intensive and often subjective, and semantics is multifaceted. however, to avoid manual annotation, one could make use of socalled 'vector-based models of meaning' or 'word embeddings', which retrieve computational representations of meaning in a bottom-up manner from a large, unannotated dataset @xcite . in the context of ancient greek, exploratory studies of vector-based models for detecting onomasiology have begun to emerge, starting from the premise that these models can be harnessed to identify words bearing a similar or related meaning to a given target word @xcite @xcite . in this case, if the researcher already knows some terms that can express a particular concept (say γλῶσσα and φωνή for the concept 'language'), they can use these models to look for terms that are similar to these target words and by doing so fully map the onomasiology of this concept. however, one complication is polysemy. when using a vector-based model 1 to find the ten nearest neighbors of the term γλῶσσα, for example, the results are all body parts, such as οὖς 'ear', ὀδούς 'tooth', ὀφθαλμός 'eye', χεῖλος 'lip' and φάρυγξ 'throat'. the explanation for this is predominantly linked to the polysemy of γλῶσσα, which can denote both 'language' and 'tongue'. the latter meaning is particularly prominent due to the corpus's extensive inclusion of medical data, which constitutes 14% of all training data, in which 'tongue' is more frequently referred to. one possible solution is wsd: if we could separate all tokens of γλῶσσα that mean 'tongue' from those meaning 'language', we could look for the nearest neighbors of γλῶσσα when only the tokens meaning 'language' are taken into account. again, vector-base models can be employed for this: indeed, several transformer-based embedding models such as bert @xcite do no longer model the 'general' meaning of a word but the meaning of a word in context. such an approach for ancient greek is discussed in @xcite , using electra @xcite as a language model. when this model was used in an unsupervised way, the results were disappointing, possibly due to data sparsity. however, when used in a supervised way, by finetuning the transformer network, decent results could be achieved with only about 150 training examples (for binary meaning distinctions) or 300 (for ternary meaning distinctions). to overcome the problems related to the acquisition bottleneck in obtaining annotated data @xcite , this paper will discuss an automated way of creating datasets for wsd, by exploiting parallel texts (greek original texts and english translations). this approach initially involves aligning sentences. subsequently, within the aligned sentences, individual words are aligned. this two-step process will enable us to annotate polysemous words in greek with english labels, thus trying to get a hold of their polysemy. 2019 ) is somewhat different in that they explore how computational methods can be used for lexical semantic change detection. focusing on three polysemous words (viz. μῦς, ἁρμονία and κόσμος), they explore their polysemy over time and genre using a bayesian topic model, and match the results to manually annotated datasets of these words. ## conclusions and perspectives in view of the extensive research conducted on wsd for modern languages, the comparative neglect of classical languages is striking. however, significant progress can be made in the near future to rectify this disparity, thanks in part to the comprehensive philological studies conducted in the past. with a robust lexicographical tradition replete with translated example sentences, and a prolific translation history, classical language resources, once available in a digital shape, have word alignment, the f1 score improved from 59.6 to 70.6, while the aer dropped from 35.9 to 24.0. the methodology presented in this paper appears to be a promising means to achieve our goals -coming to an onomasiological disclosure of the ancient greek corpus. a critical prerequisite, however, is the availability of a substantial volume of data, suggesting that the approach is effective predominantly for frequently used words. apart from this, we believe that this approach holds intrinsic value. for texts that have digital english translations available, we can make educated predictions regarding the meanings of the individual tokens. additionally, this approach provides insights into the distribution of word senses as distinguished by lexicographers in ancient greek.