8,9-POS tagging and HMMs

Use Hidden Markov Models to do POS tagging Notation: Sequence of observation overtime (sentence): $ O=o_1\dots o_T $ Sequence of states (tags): $Q=q^1\dots q^N$ Sequence states over time: $Q = q_1 \dots q_T$ Vocabulary size: $V$ Time step: $t$, not a tag Matrix of transition probabilities: $A$ $a_{ij}$: the prob of transitioning from state $q^i$ to $q^j$ Matrix of output probabilities: $B$ $b_i(o_t)$: the prob of emitting $o_t$ from state $q^i$ 1.