NLP for the Greek Language: A Brief Survey

Papantoniou, Katerina; Tzitzikas, Yannis

doi:10.1145/3411408.3411410

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…The works that concern the processing of the Greek language in general are not excessive (see Papantoniou and Tzitzikas (2020) for a recent survey), in comparison to the English language. However, there are quite a few works on the phonetics of the Greek language which are described in brief below.…”

Section: Background and Related Workmentioning

confidence: 99%

SoundexGR: An algorithm for phonetic matching for the Greek language

Kavros

Tzitzikas

2022

Nat. Lang. Eng.

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Text usually suffers from typos which can negatively affect various Information Retrieval and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language, this is not the case for other languages. For the Greek language, most of the existing phonetic algorithms provide rather insufficient support. For this reason, in this paper, we introduce an algorithm for phonetic matching designed for the Greek language: we start from the original Soundex and we redesign and extend it for accommodating the Greek language’s phonetic rules, ending up to a family of algorithms, that we call ${\tt Soundex}_{GR}$ . Then, we report various experimental results showcasing how the algorithm behaves in different scenarios, and we provide comparative results for various parameters of the algorithm for revealing the trade-off between precision and recall in datasets with different kinds of errors. We also provide comparative results with matching using stemming, full phonemic transcription, and edit distance, that demonstrate that ${\tt Soundex}_{GR}$ performs better (indicatively, it achieves F-Score over 95% in collections of similar-sounded words). The simplicity, efficiency, and effectiveness of the proposed algorithm make it applicable and adaptable to a wide range of tasks.

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Section: Background and Related Workmentioning

confidence: 99%

SoundexGR: An algorithm for phonetic matching for the Greek language

Kavros

Tzitzikas

2022

Nat. Lang. Eng.

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“…Greek, like many other European languages, is a lower-density language with few linguistic resources and knowledge bases for basic text mining and natural language processing tasks. Recent works on Greek NLP [23,24] survey the work of Greek researchers on a wide categorization of NLP tasks, which range from syntactic and morphological analysis to generic and domain-specific applications. It is evident in these surveys that research efforts focus on the following: (i) developing NLP software and training specific models for each task, or extending popular NLP software to support the Greek language, (ii) annotating training corpora that are used for the tasks (usually for machine learning, supervised, tasks such as Named-Entity Recognition (NER), POS tagging, or sentiment classification), (iii) training embeddings in large Greek corpora, in order to capture the morphology and semantics of the language.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches recorded so far are supervised and either employ feature extraction techniques (including text-based and POS-based features) and sentiment lexicons, or feature learning techniques and Greek word or sentence embeddings as a basis. A brief survey of sentiment analysis from Greek text was made available by Papantoniou et al [23]. A large-scale approach to sentiment analysis for brand monitoring in Greek social media has been presented by Petasis et al [36] in the form of an automated Software as a Service (SaaS) application.…”

Section: Nlp Models and Softwarementioning

confidence: 99%

A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

Alexandridis

Varlamis

Korovesis³

et al. 2021

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As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.

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