English-French-Lithuanian parallel corpus of EU financial documents

Smirnova, O.I.; Rackevičienė, Sigita

doi:10.7220/20.500.12259/240310

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee (2011) used an interesting approach, for Korean and English, to improve financial phrase translation, but the corpora are comparable without being really parallel. There are some parallel corpora about finance, with a limited size, such as Smirnova and Rackevičienė (2020), who made a dataset of European documents in English translated to French and Lithuanian related to finance, but the size is relatively small, consisting of 154 documents from 2010 to 2014. Bick and Barreiro (2015) made a Portuguese-English parallel dataset of about 40,000 sentences in the Legal-Financial domain, coming from a company translation memory.…”

Section: Financementioning

confidence: 99%

“…Concerning Chinese-English, Chang (2004) from Peking University made one of the first large scale Chinese-English parallel corpora from HTML files with alignments at the paragraph and sentence levels, leading to a size of 10 million Chinese characters about different genres (news, technical articles, subtitles). Concerning the domain of finance, there are some small corpora for different pairs of languages, but not Chinese-English, (Arcan, Thomas, de Brandt, & Buitelaar, 2013;Bick & Barreiro, 2015;Smirnova & Rackevičienė, 2020;Tiedemann, 2012;Volk, Amrhein, Aepli, Müller, & Ströbel, 2016). The largest one is the SEDAR dataset, 1 containing 8.6 million French-English sentence pairs in the finance domain from PDF files of the regulations of the province of Quebec (Ghaddar & Langlais, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mining an English-Chinese parallel Dataset of Financial News

Turenne

Chen

Fan

et al. 2022

JOHD

View full text Add to dashboard Cite

Parallel text datasets are a valuable for educational purposes, machine translation, and cross-language information retrieval, but few are domain-oriented. We have created a Chinese-English parallel dataset in the domain of finance technology, using the Financial Times website, from which we grabbed 60,473 news items from between 2007 and 2021. This dataset is a bilingual Chinese-English parallel dataset of news in the domain of finance. It is open access in its original state without transformation, and has been made not for machine translation as has been used, but for intelligent mining, in which we conducted many experiments using up-to-date text mining techniques: clustering (topic modeling, community detection, k-means), topic prediction (naive Bayes, SVM, LSTM, Bert), and pattern discovery (dictionary based, time series). We present the usage of these techniques as a framework for other studies, not only as an application but with an interpretation.

show abstract

Section: Financementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%