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    <titleInfo>
        <title>Extracting Lexically Divergent Paraphrases from Twitter</title>
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        <namePart type="given">Wei</namePart>
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        <namePart type="given">Yangfeng</namePart>
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    <abstract>We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter. We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning. Using this principled latent variable model alone, we achieve the performance competitive with a state-of-the-art method which combines a latent space model with a feature-based supervised classifier. Our model also captures lexically divergent paraphrases that differ from yet complement previous methods; combining our model with previous work significantly outperforms the state-of-the-art. In addition, we present a novel annotation methodology that has allowed us to crowdsource a paraphrase corpus from Twitter. We make this new dataset available to the research community.</abstract>
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%0 Journal Article
%T Extracting Lexically Divergent Paraphrases from Twitter
%A Xu, Wei
%A Ritter, Alan
%A Callison-Burch, Chris
%A Dolan, William B.
%A Ji, Yangfeng
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F xu-etal-2014-extracting
%X We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter. We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning. Using this principled latent variable model alone, we achieve the performance competitive with a state-of-the-art method which combines a latent space model with a feature-based supervised classifier. Our model also captures lexically divergent paraphrases that differ from yet complement previous methods; combining our model with previous work significantly outperforms the state-of-the-art. In addition, we present a novel annotation methodology that has allowed us to crowdsource a paraphrase corpus from Twitter. We make this new dataset available to the research community.
%R 10.1162/tacl_a_00194
%U https://aclanthology.org/Q14-1034/
%U https://doi.org/10.1162/tacl_a_00194
%P 435-448

Markdown (Informal)

[Extracting Lexically Divergent Paraphrases from Twitter](https://aclanthology.org/Q14-1034/) (Xu et al., TACL 2014)

• Extracting Lexically Divergent Paraphrases from Twitter (Xu et al., TACL 2014)

ACL

• Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan, and Yangfeng Ji. 2014. Extracting Lexically Divergent Paraphrases from Twitter. Transactions of the Association for Computational Linguistics, 2:435–448.