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    <titleInfo>
        <title>Predicting Informativeness of Semantic Triples</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts of biomedical publications to create a training corpus of informative and important semantic triples based on the notion that the main contributions of an article are summarized in its abstract. This corpus is used to train a deep learning classifier to identify important triples, and we suggest that an importance ranking for semantic triples could also be generated.</abstract>
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%0 Conference Proceedings
%T Predicting Informativeness of Semantic Triples
%A Preiss, Judita
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F preiss-2021-predicting
%X Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts of biomedical publications to create a training corpus of informative and important semantic triples based on the notion that the main contributions of an article are summarized in its abstract. This corpus is used to train a deep learning classifier to identify important triples, and we suggest that an importance ranking for semantic triples could also be generated.
%U https://aclanthology.org/2021.ranlp-1.126
%P 1124-1129

Markdown (Informal)

[Predicting Informativeness of Semantic Triples](https://aclanthology.org/2021.ranlp-1.126) (Preiss, RANLP 2021)

• Predicting Informativeness of Semantic Triples (Preiss, RANLP 2021)

ACL

• Judita Preiss. 2021. Predicting Informativeness of Semantic Triples. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1124–1129, Held Online. INCOMA Ltd..