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Each emission is assumed to be independent (conditionally on the latent label), so the Baum-Welch algorithm is the same, except that the likelihood function computes a log-sum over the emissions of each labelling function instead of considering just one emission. This is implemented in the file labelling.py
. Hope it answers your question!
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Sorry to bother you again! It seems that to aggregate multiple labelling functions, we have to compute probs through the code
probs = np.dot(X[:,source_index,:], self.emission_probs[source_index,:,:].T) in the file labelling.py. This confuses me since the emission_probs means the probability from states to observations while X is the observation sequences. Is there something wrong or I misunderstand it?
from weak-supervision-for-ner.
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