DECHE and the Welsh National Corpus Portal

Prys, Delyth; Jones, D.; Roberts, Mared

doi:10.3115/v1/w14-4611

Cited by 5 publications

(2 citation statements)

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“…To prevent duplication of the previous Welsh Wikipedia corpus, we removed all Wikipedia articles. DECHE-The Digitization, E-publishing, and Electronic Corpus (DECHE) project produces e-books out of Welsh language scholarly, academic books which are out of print and unlikely to be reprinted in traditional paper format[44]. Candidates for producing as e-books are nominated by lecturers working through the medium of Welsh and prioritized by the Coleg Cymraeg Cenedlaethol, who fund the project.…”

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confidence: 99%

English–Welsh Cross-Lingual Embeddings

et al. 2021

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Cross-lingual embeddings are vector space representations where word translations tend to be co-located. These representations enable learning transfer across languages, thus bridging the gap between data-rich languages such as English and others. In this paper, we present and evaluate a suite of cross-lingual embeddings for the English–Welsh language pair. To train the bilingual embeddings, a Welsh corpus of approximately 145 M words was combined with an English Wikipedia corpus. We used a bilingual dictionary to frame the problem of learning bilingual mappings as a supervised machine learning task, where a word vector space is first learned independently on a monolingual corpus, after which a linear alignment strategy is applied to map the monolingual embeddings to a common bilingual vector space. Two approaches were used to learn monolingual embeddings, including word2vec and fastText. Three cross-language alignment strategies were explored, including cosine similarity, inverted softmax and cross-domain similarity local scaling (CSLS). We evaluated different combinations of these approaches using two tasks, bilingual dictionary induction, and cross-lingual sentiment analysis. The best results were achieved using monolingual fastText embeddings and the CSLS metric. We also demonstrated that by including a few automatically translated training documents, the performance of a cross-lingual text classifier for Welsh can increase by approximately 20 percent points.

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confidence: 99%

English–Welsh Cross-Lingual Embeddings

et al. 2021

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“…To prevent duplication of data we removed all Wikipedia articles from this corpus before using it in the present study. • DECHE-The Digitisation, E-publishing and Electronic Corpus (DECHE) project publishes e-versions of Welsh scholarly books that are out of print and unlikely to be re-printed in traditional paper format [26]. Books are nominated by lecturers working through the medium of Welsh and prioritised by the Coleg Cymraeg Cenedlaethol, which funds the project.…”

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confidence: 99%

Creating Welsh Language Word Embeddings

et al. 2021

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Word embeddings are representations of words in a vector space that models semantic relationships between words by means of distance and direction. In this study, we adapted two existing methods, word2vec and fastText, to automatically learn Welsh word embeddings taking into account syntactic and morphological idiosyncrasies of this language. These methods exploit the principles of distributional semantics and, therefore, require a large corpus to be trained on. However, Welsh is a minoritised language, hence significantly less Welsh language data are publicly available in comparison to English. Consequently, assembling a sufficiently large text corpus is not a straightforward endeavour. Nonetheless, we compiled a corpus of 92,963,671 words from 11 sources, which represents the largest corpus of Welsh. The relative complexity of Welsh punctuation made the tokenisation of this corpus relatively challenging as punctuation could not be used for boundary detection. We considered several tokenisation methods including one designed specifically for Welsh. To account for rich inflection, we used a method for learning word embeddings that is based on subwords and, therefore, can more effectively relate different surface forms during the training phase. We conducted both qualitative and quantitative evaluation of the resulting word embeddings, which outperformed previously described word embeddings in Welsh as part of larger study including 157 languages. Our study was the first to focus specifically on Welsh word embeddings.

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