Search Results for author: Richard Eckart de Castilho

Found 16 papers, 3 papers with code

The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation

no code implementations COLING 2018 Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, Iryna Gurevych

We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e. g., concept linking, fact linking, knowledge base population, semantic frame annotation).

Active Learning Entity Linking +2

A tool for extracting sense-disambiguated example sentences through user feedback

no code implementations EACL 2017 Beto Boullosa, Richard Eckart de Castilho, Alex Geyken, er, Lothar Lemnitzer, Iryna Gurevych

This paper describes an application system aimed to help lexicographers in the extraction of example sentences for a given headword based on its different senses.

General Classification

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

no code implementations WS 2017 Richard Eckart de Castilho, Nancy Ide, Emanuele Lapponi, Stephan Oepen, Keith Suderman, Erik Velldal, Marc Verhagen

We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.

Automatic Analysis of Flaws in Pre-Trained NLP Models

1 code implementation WS 2016 Richard Eckart de Castilho

Our work 1) allows model consumers to better assess whether a model is suitable for their task, 2) enables tool and model creators to sanity-check their models before distributing them, and 3) enables improvements in tool interoperability by performing automatic adjustments of normalization or other pre-processing based on the models used.

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