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次へ: Conclusion for Ergodic HMM 上へ: Automatic Acquisition on Stochastic 戻る: Analysis of an 8-state

Analysis of a 16-state Ergodic HMM

The results of a 16-state Ergodic HMM are shown in Figure  2 . In this example, a lot of state transitions are shown for only one POS. In addition, the initial state value has two states (○ 2,○ 3). The 16-state Ergodic HMM is more refined than the 8-state Ergodic HMM.

図 2: Results of a 16-state Ergodic HMM analysis
\begin{figure*}\begin{center}
\epsfig{file=16st-L-model.ps,height=90mm}\end{center}\end{figure*}

The results of this analysis are shown in the following for each POS.

  1. Interjection

    Interjections have outputs from (○ 2, ○ 3) which have a high initial value like the 8-state Ergodic HMM. However, the initial value has two state (○ 2, ○ 3), and does not have a self loop of state 2.

  2. Common noun

    Common nouns are mainly output from ○ 567 to ○ 1912.

    Common nouns are output from states ○ 67. Other nouns are output from state ○ 5; this state has many kinds of nouns.

  3. Conjugate word

    In the 8-state Ergodic HMM, mainly the conjunctions are grouped, However, in the 16-state Ergodic HMM, the POSs are grouped.

    Verbs are output from ○ 85, conjoining verbs are output from states ○ 04, and auxiliary verbs are output from ○ 01113.

  4. Case particle

    Case particles are output from ○ 8912. Each transition is another word (like the 8-state Ergodic HMM). However, the 16-state Ergodic HMM has more precise clustering. Examples are shown as follows.




    The case particle of /de/ and /ni/ or /ga/ and /wo/ have the same transitions as in the case of the 8-state Ergodic HMM. However, these particles are divided in the 16-state Ergodic HMM.


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次へ: Conclusion for Ergodic HMM 上へ: Automatic Acquisition on Stochastic 戻る: Analysis of an 8-state
Jin'ichi Murakami 平成13年1月19日