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20162023
most citedSemantic Tagging with Deep Residual Networks

62 citations · 199 across the 49 of their papers we have counts for

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Showing 2016Show all

6 papers · 1 filter

cs.CL2016

When silver glitters more than gold: Bootstrapping an Italian part-of-speech tagger for Twitter

Barbara Plank, Malvina Nissim

We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter data, in the context of the Evalita 2016 PoSTWITA shared task. We show that training the tagger on nati…

cs.CL2016★ 62 cited

Semantic Tagging with Deep Residual Networks

Johannes Bjerva, Barbara Plank, Johan Bos

We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets…

cs.CL2016★ 9 cited

Keystroke dynamics as signal for shallow syntactic parsing

Barbara Plank

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that…

cs.CL2016★ 17 cited

What to do about non-standard (or non-canonical) language in NLP

Barbara Plank

Real world data differs radically from the benchmark corpora we use in natural language processing (NLP). As soon as we apply our technologies to the real world, performance drops.…

cs.CL2016

Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss

Barbara Plank, Anders Søgaard, Yoav Goldberg

Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input r…

cs.CL2016★ 10 cited

Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures

Raffaella Bernardi, Ruket Cakici, Desmond Elliott +6

Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language pr…