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        <title>Domain-Adaptable Hybrid Generation of RDF Entity Descriptions</title>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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    <abstract>RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity. While they can be useful as a starting point for generating descriptions of entities, they often miss important information about an entity that cannot be captured as simple relations. In addition, generic approaches to generation from RDF cannot capture the unique style and content of specific domains. We describe a framework for hybrid generation of entity descriptions, which combines generation from RDF data with text extracted from a corpus, and extracts unique aspects of the domain from the corpus to create domain-specific generation systems. We show that each component of our approach significantly increases the satisfaction of readers with the text across multiple applications and domains.</abstract>
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%0 Conference Proceedings
%T Domain-Adaptable Hybrid Generation of RDF Entity Descriptions
%A Biran, Or
%A McKeown, Kathleen
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F biran-mckeown-2017-domain
%X RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity. While they can be useful as a starting point for generating descriptions of entities, they often miss important information about an entity that cannot be captured as simple relations. In addition, generic approaches to generation from RDF cannot capture the unique style and content of specific domains. We describe a framework for hybrid generation of entity descriptions, which combines generation from RDF data with text extracted from a corpus, and extracts unique aspects of the domain from the corpus to create domain-specific generation systems. We show that each component of our approach significantly increases the satisfaction of readers with the text across multiple applications and domains.
%U https://aclanthology.org/I17-1031
%P 306-315

Markdown (Informal)

[Domain-Adaptable Hybrid Generation of RDF Entity Descriptions](https://aclanthology.org/I17-1031) (Biran & McKeown, IJCNLP 2017)

• Domain-Adaptable Hybrid Generation of RDF Entity Descriptions (Biran & McKeown, IJCNLP 2017)

ACL

• Or Biran and Kathleen McKeown. 2017. Domain-Adaptable Hybrid Generation of RDF Entity Descriptions. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 306–315, Taipei, Taiwan. Asian Federation of Natural Language Processing.