@inproceedings{preiss-2021-predicting,
title = "Predicting Informativeness of Semantic Triples",
author = "Preiss, Judita",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.126",
pages = "1124--1129",
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.",
}
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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
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 sep
%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)
ACL