An unsupervised topic segmentation model incorporating word order

@inproceedings{Jameel2013AnUT,
  title={An unsupervised topic segmentation model incorporating word order},
  author={Shoaib Jameel and Wai Lam},
  booktitle={SIGIR},
  year={2013}
}
We present a new unsupervised topic discovery model for a collection of text documents. In contrast to the majority of the state-of-the-art topic models, our model does not break the document's structure such as paragraphs and sentences. In addition, it preserves word order in the document. As a result, it can generate two levels of topics of different granularity, namely, segment-topics and word-topics. In addition, it can generate n-gram words in each topic. We also develop an approximate… CONTINUE READING
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