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    <titleInfo>
        <title>Event Ordering with a Generalized Model for Sieve Prediction Ranking</title>
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    <name type="personal">
        <namePart type="given">Bill</namePart>
        <namePart type="family">McDowell</namePart>
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    <name type="personal">
        <namePart type="given">Nathanael</namePart>
        <namePart type="family">Chambers</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Ororbia II</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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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>This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5% relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture‘s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8% relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures.</abstract>
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%0 Conference Proceedings
%T Event Ordering with a Generalized Model for Sieve Prediction Ranking
%A McDowell, Bill
%A Chambers, Nathanael
%A Ororbia II, Alexander
%A Reitter, David
%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 mcdowell-etal-2017-event
%X This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5% relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture‘s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8% relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures.
%U https://aclanthology.org/I17-1085/
%P 843-853

Markdown (Informal)

[Event Ordering with a Generalized Model for Sieve Prediction Ranking](https://aclanthology.org/I17-1085/) (McDowell et al., IJCNLP 2017)

• Event Ordering with a Generalized Model for Sieve Prediction Ranking (McDowell et al., IJCNLP 2017)

ACL

• Bill McDowell, Nathanael Chambers, Alexander Ororbia II, and David Reitter. 2017. Event Ordering with a Generalized Model for Sieve Prediction Ranking. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 843–853, Taipei, Taiwan. Asian Federation of Natural Language Processing.