J
Joachim Wagner
Researcher at Dublin City University
Publications - 43
Citations - 1165
Joachim Wagner is an academic researcher from Dublin City University. The author has contributed to research in topics: Parsing & Treebank. The author has an hindex of 14, co-authored 41 publications receiving 1041 citations.
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Proceedings ArticleDOI
Code Mixing: A Challenge for Language Identification in the Language of Social Media
TL;DR: A new dataset is described, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi, and it is found that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.
Proceedings ArticleDOI
DCU: Aspect-based Polarity Classification for SemEval Task 4
Joachim Wagner,Piyush Arora,Santiago Cortes,Utsab Barman,Dasha Bogdanova,Jennifer Foster,Lamia Tounsi +6 more
TL;DR: The DCU team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest on the laptop test set.
Proceedings Article
#hardtoparse: POS tagging and parsing the twitterverse
Jennifer Foster,Özlem Çetinoǧlu,Joachim Wagner,Joseph Le Roux,Stephen Hogan,Joakim Nivre,Deirdre Hogan,Josef van Genabith +7 more
TL;DR: Retraining Malt on dependency trees produced by a state-of-the-art phrase structure parser, which has itself been self-trained on Twitter material, results in a significant improvement and is analysed by examining in detail the effect of the retraining on individual dependency types.
Proceedings Article
From News to Comment: Resources and Benchmarks for Parsing the Language of Web 2.0
Jennifer Foster,Özlem Çetinoğlu,Joachim Wagner,Joseph Le Roux,Joakim Nivre,Deirdre Hogan,Josef van Genabith +6 more
TL;DR: It is found that the Wall-Street-Journal-trained statistical parsers have a particular problem with tweets and that a substantial part of this problem is related to POS tagging accuracy.
Journal ArticleDOI
Judging Grammaticality: Experiments in Sentence Classification
TL;DR: It is demonstrated that the combination of information from a variety of linguistic sources is helpful, the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme.