Search Results for author: Michael Strobl

Found 4 papers, 2 papers with code

Named Entity Recognition for Partially Annotated Datasets

no code implementations19 Apr 2022 Michael Strobl, Amine Trabelsi, Osmar Zaiane

The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i. e. the class of all words for all entities is known.

named-entity-recognition Named Entity Recognition +1

FREDA: Flexible Relation Extraction Data Annotation

1 code implementation14 Apr 2022 Michael Strobl, Amine Trabelsi, Osmar Zaiane

To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required.

Relation Relation Extraction

WEXEA: Wikipedia EXhaustive Entity Annotation

no code implementations LREC 2020 Michael Strobl, Amine Trabelsi, Osmar Zaiane

Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text.

named-entity-recognition Named Entity Recognition +2

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