7.00.02 - Input - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)

You can use the HMMDecoder function to decode the parts of speech (adjective, noun, verb, and so on.) for a word set, if the set of phrases or words have been trained using a HMMSupervised or HMMUnsupervisedLearner. Assume that you have a set of phrases (shown in the following table) whose parts of speech are unknown and you have the three trained state tables (initial, state_transition and emission) readily available.

In this example, the parts of speech correspond to the hidden states of the HMM function. There are two hidden states in this example: A(Adjective) and N(noun). HMMDecoder can be used to find these parts of speech.

HMMDecoder Example 3 Input Table phrases
model phrase_id word
1 1 clown
1 1 crazy
1 1 killer
1 1 problem
1 2 nice
1 2 weather

The following is a table of initial states:

HMMDecoder Example 3 Input Table initial
model tag probability
1 A 0.25
1 N 0.75

The following is a table of state transitions:

HMMDecoder Example 3 Input Table state_transition
model from_tag to_tag probability
1 A A 0
1 A N 1
1 N A 0.5
1 N N 0.5

The following is a table of emissions:

HMMDecoder Example 3 Input Table emission
model tag word probability
1 A clown 0
1 N clown 0.4
1 A crazy 1
1 N crazy 0
1 A killer 0
1 N killer 0.3
1 A problem 0
1 N problem 0.3