Search Results for author: Marten Postma

Found 13 papers, 1 papers with code

Large-scale Cross-lingual Language Resources for Referencing and Framing

no code implementations LREC 2020 Piek Vossen, Filip Ilievski, Marten Postma, Antske Fokkens, Gosse Minnema, Levi Remijnse

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language.

Combining Conceptual and Referential Annotation to Study Variation in Framing

no code implementations LREC 2020 Marten Postma, Levi Remijnse, Filip Ilievski, Antske Fokkens, Sam Titarsolej, Piek Vossen

The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.

SemEval-2018 Task 5: Counting Events and Participants in the Long Tail

no code implementations SEMEVAL 2018 Marten Postma, Filip Ilievski, Piek Vossen

This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity.

Word Sense Disambiguation

Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?

no code implementations9 Dec 2017 Minh Le, Marten Postma, Jacopo Urbani

Recently, Yuan et al. (2016) have shown the effectiveness of using Long Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD).

Word Sense Disambiguation

Semantic overfitting: what `world' do we consider when evaluating disambiguation of text?

no code implementations COLING 2016 Filip Ilievski, Marten Postma, Piek Vossen

Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time.

More is not always better: balancing sense distributions for all-words Word Sense Disambiguation

1 code implementation COLING 2016 Marten Postma, Ruben Izquierdo Bevia, Piek Vossen

Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses, which is mainly caused by the difference in sense distributions between training and test data.

Word Sense Disambiguation

Addressing the MFS Bias in WSD systems

no code implementations LREC 2016 Marten Postma, Ruben Izquierdo, Eneko Agirre, German Rigau, Piek Vossen

Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses.

Word Sense Disambiguation

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