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Disambiguation of "Dinh, Erik-Lân Do"

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Filter and Annotate: Towards Automatic Identification of Genuine Metaphoricity.

, , and . eScience, page 308-309. IEEE Computer Society, (2018)

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To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Erik Eppinger University of Stuttgart

Erik Hesselmeier University of Stuttgart

Dr. Erik Poloni University of Stuttgart

Erik Wimmer University of Stuttgart

Konrad Langenbeck

 

Other publications of authors with the same name

Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning., , , and . ACL (1), page 5716-5728. Association for Computational Linguistics, (2019)Filter and Annotate: Towards Automatic Identification of Genuine Metaphoricity., , and . eScience, page 308-309. IEEE Computer Society, (2018)EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION., , , , and . SemEval@ACL, page 942-946. Association for Computational Linguistics, (2017)Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations., , and . EMNLP, page 1412-1424. Association for Computational Linguistics, (2018)In-tool Learning for Selective Manual Annotation in Large Corpora., , and . ACL (System Demonstrations), page 13-18. The Association for Computer Linguistics, (2015)One Size Fits All? A simple LSTM for non-literal token and construction-level classification., , and . LaTeCH@COLING, page 70-80. Association for Computational Linguistics, (2018)Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection., , and . COLING, page 1558-1569. Association for Computational Linguistics, (2018)Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks., , , and . COLING, page 1703-1714. ACL, (2016)OFAI-UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning., , , and . IberLEF@SEPLN, volume 2421 of CEUR Workshop Proceedings, page 180-190. CEUR-WS.org, (2019)