Ghent University Academic Bibliography

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Conference Paper
open access

An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus

Luna De Bruyne (UGent) , Orphée De Clercq (UGent) and Veronique Hoste (UGent)

(2020) Proceedings of The 12th Language Resources and Evaluation Conference. p.1636-1644

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Journal Article
A1
open access

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Ayla Rigouts Terryn (UGent) , Veronique Hoste (UGent) and Els Lefever (UGent)

(2020) LANGUAGE RESOURCES AND EVALUATION. 54(2). p.385-418

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Estimating word-level quality of statistical machine translation output using monolingual information alone

Arda Tezcan (UGent) , Veronique Hoste (UGent) and Lieve Macken (UGent)

(2020) NATURAL LANGUAGE ENGINEERING. 26(1). p.73-94

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News diversity and recommendation systems : setting the interdisciplinary scene

Glen Joris (UGent) , Camiel Colruyt (UGent) , Judith Vermeulen (UGent) , Stefaan Vercoutere (UGent) , Frederik De Grove (UGent) , Kristin Van Damme (UGent) , Orphée De Clercq (UGent) , Cynthia Van Hee (UGent) , Lieven De Marez (UGent) , Veronique Hoste (UGent) , et al.

(2020) Privacy and Identity Management. Data for Better Living : AI and Privacy. In IFIP Advances in Information and Communication Technology 576. p.90-105

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Conference Paper
open access

TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset

Ayla Rigouts Terryn (UGent) , Veronique Hoste (UGent) , Patrick Drouin and Els Lefever (UGent)

(2020) Proceedings of the 6th International Workshop on Computational Terminology (COMPUTERM 2020). p.85-94

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Estimating post-editing time using a gold-standard set of machine translation errors

Arda Tezcan (UGent) , Veronique Hoste (UGent) and Lieve Macken (UGent)

(2019) COMPUTER SPEECH AND LANGUAGE. 55. p.120-144

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The trade-off between quantity and quality : comparing a large web corpus and a small focused corpus for medical terminology extraction

Veronique Hoste (UGent) , Klaar Vanopstal (UGent) , Ayla Rigouts Terryn (UGent) and Els Lefever (UGent)

(2019) ACROSS LANGUAGES AND CULTURES. 20(2). p.197-211

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Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat

Ayla Rigouts Terryn (UGent) , Patrick Drouin, Veronique Hoste (UGent) and Els Lefever (UGent)

(2019) Proceedings of Recent Advances in Natural Language Processing (RANLP 2019) : natural language processing in a deep learining world. p.1013-1022

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Comparing MT approaches for text normalization

Claudia Matos Veliz (UGent) , Orphée De Clercq (UGent) and Veronique Hoste (UGent)

(2019) Proceedings of Recent Advances in Natural Language Processing (RANLP 2019) : natural language processing in a deep learining world. p.740-749

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Conference Paper
open access

Benefits of data augmentation for NMT-based text normalization of user-generated content

Claudia Matos Veliz (UGent) , Orphée De Clercq (UGent) and Veronique Hoste (UGent)

(2019) Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). p.275-285

Academic Bibliography

An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Estimating word-level quality of statistical machine translation output using monolingual information alone

News diversity and recommendation systems : setting the interdisciplinary scene

TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset

Estimating post-editing time using a gold-standard set of machine translation errors

The trade-off between quantity and quality : comparing a large web corpus and a small focused corpus for medical terminology extraction

Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat

Comparing MT approaches for text normalization

Benefits of data augmentation for NMT-based text normalization of user-generated content

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