Applying Distributional Semantic Models to a Historical Corpus of a Highly Inflected Language: the Case of Ancient Greek

Abstract

So-called “distributional” language models have become dominant in research on the computational modelling of lexical semantics. This paper investigates how well such models perform on Ancient Greek, a highly inflected historical language. It compares several ways of computing such distributional models on the basis of various context features (including both bag-of-words features and syntactic dependencies). The performance is assessed by evaluating how well these models are able to retrieve semantically similar words to a given target word, both on a benchmark we designed ourselves as well as on several independent benchmarks. It finds that dependency features are particularly useful to calculate distributional vectors for Ancient Greek (although the level of granularity that these dependency features should have is still open to discussion) and discusses possible ways for further improvement, including addressing problems related to polysemy and genre differences.