More Romanian word embeddings from the RETEROM project

Automatically learned vector representations of words, also known as "word embeddings", are becoming a basic building block for more and more natural language processing algorithms. There are different ways and tools for constructing word embeddings. Most of the approaches rely on raw text...

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Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Păiş, Vasile, Tufiş, Dan
Format: Article
Language:English
Subjects:
Algorithms
Feature extraction
Graphical representations
Morphology
Natural language processing
Semantics
Speech
Texts
Words (language)
Online Access:Get full text
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Summary:Automatically learned vector representations of words, also known as "word embeddings", are becoming a basic building block for more and more natural language processing algorithms. There are different ways and tools for constructing word embeddings. Most of the approaches rely on raw texts, the construction items being the word occurrences and/or letter n-grams. More elaborated research is using additional linguistic features extracted after text preprocessing. Morphology is clearly served by vector representations constructed from raw texts and letter n-grams. Syntax and semantics studies may profit more from the vector representations constructed with additional features such as lemma, part-of-speech, syntactic or semantic dependants associated with each word. One of the key objectives of the ReTeRom project is the development of advanced technologies for Romanian natural language processing, including morphological, syntactic and semantic analysis of text. As such, we plan to develop an open-access large library of ready-to-use word embeddings sets, each set being characterized by different parameters: used features (wordforms, letter n-grams, lemmas, POSes etc.), vector lengths, window/context size and frequency thresholds. To this end, the previously created sets of word embeddings (based on word occurrences) on the CoRoLa corpus (Păiş and Tufiş, 2018) are and will be further augmented with new representations learned from the same corpus by using specific features such as lemmas and parts of speech. Furthermore, in order to better understand and explore the vectors, graphical representations will be available by customized interfaces.
ISSN:2331-8422