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A Discriminative Latent Variable Model for Online Clustering

Rajhans Samdani, Kai-Wei Chang, and Dan Roth, in ICML, 2014.

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Abstract

This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, L3M outperforms several existing online as well as batch supervised clustering techniques.

Bib Entry

@inproceedings{samdani2014discriminative,
  author = {Samdani, Rajhans and Chang, Kai-Wei and Roth, Dan},
  title = {A Discriminative Latent Variable Model for Online Clustering},
  booktitle = {ICML},
  year = {2014}
}

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    Rajhans Samdani, Kai-Wei Chang, and Dan Roth, in ICML, 2014.
    Full Text Slides Demo Abstract BibTeX Details 
    This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, L3M outperforms several existing online as well as batch supervised clustering techniques.
    @inproceedings{samdani2014discriminative,
  author = {Samdani, Rajhans and Chang, Kai-Wei and Roth, Dan},
  title = {A Discriminative Latent Variable Model for Online Clustering},
  booktitle = {ICML},
  year = {2014}
}
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  2. A Constrained Latent Variable Model for Coreference Resolution

    Kai-Wei Chang, Rajhans Samdani, and Dan Roth, in EMNLP, 2013.
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    @inproceedings{ChangSaRo13,
  author = {Chang, Kai-Wei and Samdani, Rajhans and Roth, Dan},
  title = {A Constrained Latent Variable Model for Coreference Resolution},
  booktitle = {EMNLP},
  year = {2013}
}
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  3. Illinois-Coref: The UI System in the CoNLL-2012 Shared Task

    Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Mark Sammons, and Dan Roth, in CoNLL Shared Task, 2012.
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  author = {Chang, Kai-Wei and Samdani, Rajhans and Rozovskaya, Alla and Sammons, Mark and Roth, Dan},
  title = {Illinois-Coref: The UI System in the CoNLL-2012 Shared Task},
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  4. Inference Protocols for Coreference Resolution

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    This paper presents Illinois-Coref, a system for coreference resolution that participated in the CoNLL-2011 shared task. We investigate two inference methods, Best-Link and All-Link, along with their corresponding, pairwise and structured, learning protocols. Within these, we provide a flexible architecture for incorporating linguistically-motivated constraints, several of which we developed and integrated. We compare and evaluate the inference approaches and the contribution of constraints, analyze the mistakes of the system, and discuss the challenges of resolving coreference for the OntoNotes-4.0 data set.
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  author = {Chang, Kai-Wei and Samdani, Rajhans and Rozovskaya, Alla and Rizzolo, Nick and Sammons, Mark and Roth, Dan},
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