COREA: Coreference Resolution for Extracting Answers for Dutch
Citations Over Time
Abstract
Many natural language processing applications can benefit from the identification of coreference relations. For example, in information extraction and question answering, recall should in principle increase when the available information is expanded by linking expressions in a text that refer to the same discourse entity. Most current state-of-the-art systems for coreference resolution are based on supervised machine learning, and require (large) amounts of annotated data for training and testing. As these data were lacking for Dutch, the corea project had as its goal the construction of a coreference corpus for Dutch, and the development of an automatic coreference resolution system that used the corpus as training material. In this paper, we present the results of our annotation efforts, and the design of the automatic resolution system. Furthermore, we discuss various experiments that were carried out to determine the accuracy of the resolution system and the potential benefit of incorporating coreference resolution in nlp tasks
Related Papers
- → CDCAT: A multi-language cross-document entity and event coreference annotation tool(2021)5 cited
- Coreference Annotation: Whither?(2000)
- → COREA: Coreference Resolution for Extracting Answers for Dutch(2012)1 cited
- TwiConv: A Coreference-annotated Corpus of Twitter Conversations(2020)
- → Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models(2023)