Search Results for author: Bich-Ngoc Do

Found 7 papers, 0 papers with code

Parsers Know Best: German PP Attachment Revisited

no code implementations COLING 2020 Bich-Ngoc Do, Ines Rehbein

In particular, we show that using gold information for the extraction of attachment candidates as well as a missing comparison of the system{'}s output to the output of a full syntactic parser leads to an overly optimistic assessment of the results.

Neural Reranking for Dependency Parsing: An Evaluation

no code implementations ACL 2020 Bich-Ngoc Do, Ines Rehbein

We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech.

Dependency Parsing

Evaluating a Dependency Parser on DeReKo

no code implementations LREC 2020 Peter Fankhauser, Bich-Ngoc Do, Marc Kupietz

We evaluate a graph-based dependency parser on DeReKo, a large corpus of contemporary German.

What do we need to know about an unknown word when parsing German

no code implementations WS 2017 Bich-Ngoc Do, Ines Rehbein, Anette Frank

We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German.

Language Modelling POS +1

Evaluating LSTM models for grammatical function labelling

no code implementations WS 2017 Bich-Ngoc Do, Ines Rehbein

To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history.

Dependency Parsing

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