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There is a plethora of methods, tools and resources for processing text in the English language, however this is not the case for other languages, like Greek. Due to the increasing interest in NLP, and since there is a noteworthy number of works related to the processing of the Greek language, in this paper we survey the work related to the processing of Greek language. In particular, we list and briefly discuss related works, resources and tools, categorized according to various processing layers and contexts. This survey can be useful for researchers and students interested in NLP tasks, Information Retrieval and Knowledge Management for the Greek language.

NLP for the Greek Language: A Brief Survey

Free indirect discourse (FID) is one of the many varieties of reported speech. It is commonly agreed that passages in FID have to be interpreted relative to two context parameters , the narrator context C and the context of speech/thought of the protagonist c. While temporal and locational shifting indexicals have received extensive discussion in the literature, shiftable reference to the speaker is a largely neglected phenomenon, to the exception of Schlenker (1999, 2004) and Sharvit (2008). I offer an analysis of German speaker oriented particles as shifting indexicals which reveals how particles can be used to indicate speaker shifts in free indirect discourse.

Particles as Speaker Indexicals in Free Indirect Discourse

Because of the less-resourced nature of historical languages, non-standard solutions are often required for natural language processing tasks. This article introduces one such solution for historical-language lemmatization, that is the Ensemble lemmatizer for the Classical Language Toolkit, an open-source Python package that supports NLP research for historical languages. Ensemble lemmatization is the most recent development at CLTK in the repurposing and refactoring of an existing method designed for one task, specifically the backoff method as used for part-of-speech tagging, for use in a different task, namely lemmatization. This article argues for the benefits of ensemble lemmatization, specifically, flexible tool construction and the use of all available information to reach tagging decisions, and presents two use cases.

/pdf/ensemble-lemmatization-with-the-classical-language-toolkit-527ivldx9x.pdf

Ensemble lemmatization with the Classical Language Toolkit

Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. While prior work on Classical languages unanimously uses BERT, in this work we create four language models for Ancient Greek that vary along two dimensions to study their versatility for tasks of interest for Classical languages: we explore (i) encoder-only and encoder-decoder architectures using RoBERTa and T5 as strong model types, and create for each of them (ii) a monolingual Ancient Greek and a multilingual instance that includes Latin and English. We evaluate all models on morphological and syntactic tasks, including lemmatization, which demonstrates the added value of T5’s decoding abilities. We further define two probing tasks to investigate the knowledge acquired by models pre-trained on Classical texts. Our experiments provide the first benchmarking analysis of existing models of Ancient Greek. Results show that our models provide significant improvements over the SoTA. The systematic analysis of model types can inform future research in designing language models for Classical languages, including the development of novel generative tasks. We make all our models available as community resources, along with a large curated pre-training corpus for Ancient Greek, to support the creation of a larger, comparable model zoo for Classical Philology.

https://www.aclanthology.org/2023.acl-long.846.pdf

Exploring Large Language Models for Classical Philology

In this paper we present the lemmatizer that we developed for Ancient Greek: GLEM. As far as we know, GLEM is the first publicly available lemmatizer for Ancient Greek that uses POS information to disambiguate and that also assigns output to unseen words, words that are not yet in the lexicon.As the basis for the lemmatizer we used an existing memory-based learning tool, Frog, that was originally developed for Dutch and that we converted to work for Ancient Greek. As the results of Frog on Ancient Greek were rather modest, we used Frog to create a smarter lemmatizer, GLEM, that uses a lexicon look up in addition to the memory-based tool Frog. We evaluate and compare the performance of GLEM against the Frog lemmatizer and the already existing CLTK lemmatizer and observe that GLEM achieves the highest accuracy of 93% on an unseen test corpus sample. GLEM's look up component overcomes the difficulty of a relative small training set in combination with a morphologically rich language, while the memory-based learning component enables GLEM to handle unknown words.

https://dl.acm.org/doi/pdf/10.1145/3078081.3078100

A Memory-Based Lemmatizer for Ancient Greek

We present REPORTS, an annotation scheme for the annotation of speech, attitude and perception reports. Such a scheme makes it possible to annotate the various text elements involved in such reports (e.g. embedding entity, complement, complement head) and their relations in a uniform way, which in turn facilitates the automatic extraction of information on, for example, complementation and vocabulary distribution. We also present the Ancient Greek corpus RAG (Thucydides’ History of the Peloponnesian War), to which we have applied this scheme using the annotation tool BRAT. We discuss some of the issues, both theoretical and practical, that we encountered, show how the corpus helps in answering specific questions, and conclude that REPORTS fitted in well with our needs.

/pdf/annotating-speech-attitude-and-perception-reports-1au406b3d6.pdf

Annotating Speech, Attitude and Perception Reports.

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A Memory-Based Lemmatizer for Ancient Greek

Annotating Speech, Attitude and Perception Reports.