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        <title>FrameIt: Ontology Discovery for Noisy User-Generated Text</title>
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        <namePart type="given">Dan</namePart>
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        <namePart type="given">Alon</namePart>
        <namePart type="family">Halevy</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action. The ontology defining the structure is typically application dependent and in many cases it is not known a priori. We describe the FrameIt System that provides a workflow for (1) quickly discovering an ontology to model a text corpus and (2) learning an SRL model that extracts the instances of the ontology from sentences in the corpus. FrameIt exploits data that is obtained in the ontology discovery phase as weak supervision data to bootstrap the SRL model and then enables the user to refine the model with active learning. We present empirical results and qualitative analysis of the performance of FrameIt on three corpora of noisy user-generated text.</abstract>
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        <date>2018-11</date>
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%0 Conference Proceedings
%T FrameIt: Ontology Discovery for Noisy User-Generated Text
%A Iter, Dan
%A Halevy, Alon
%A Tan, Wang-Chiew
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F iter-etal-2018-frameit
%X A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action. The ontology defining the structure is typically application dependent and in many cases it is not known a priori. We describe the FrameIt System that provides a workflow for (1) quickly discovering an ontology to model a text corpus and (2) learning an SRL model that extracts the instances of the ontology from sentences in the corpus. FrameIt exploits data that is obtained in the ontology discovery phase as weak supervision data to bootstrap the SRL model and then enables the user to refine the model with active learning. We present empirical results and qualitative analysis of the performance of FrameIt on three corpora of noisy user-generated text.
%R 10.18653/v1/W18-6123
%U https://aclanthology.org/W18-6123
%U https://doi.org/10.18653/v1/W18-6123
%P 173-183

Markdown (Informal)

[FrameIt: Ontology Discovery for Noisy User-Generated Text](https://aclanthology.org/W18-6123) (Iter et al., WNUT 2018)

• FrameIt: Ontology Discovery for Noisy User-Generated Text (Iter et al., WNUT 2018)

ACL

• Dan Iter, Alon Halevy, and Wang-Chiew Tan. 2018. FrameIt: Ontology Discovery for Noisy User-Generated Text. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 173–183, Brussels, Belgium. Association for Computational Linguistics.