Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Learning for Fully Unsupervised Word Segmentation Using
  Penalized Likelihood and Model Selection   We present a novel incremental learning approach for unsupervised word
segmentation that combines features from probabilistic modeling and model
selection. This includes super-additive penalties for addressing the cognitive
burden imposed by long word formation, and new model selection criteria based
on higher-order generative assumptions. Our approach is fully unsupervised; it
relies on a small number of parameters that permits flexible modeling and a
mechanism that automatically learns parameters from the data. Through
experimentation, we show that this intricate design has led to top-tier
performance in both phonemic and orthographic word segmentation.

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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas