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We use a Bayesian Adaptive Trigram Model for our Textual Keyword extraction phase.
We use a probabilistic model which works on a triplet of words (E,T,P) and uses Keyword Frequencies (K.F.) for the calculation of the probabilities.
We apply an ad-hoc graph to represent our knowledge of words and their association.
First we train our Keyword model using a weight matrix W and a word vector V.
We have a weight matrix W (around 50000) representing the words’ importance based on their frequency (Keyword Frequencies) in a given corpus.
We construct V from S.V. = W*S where S is the frequency of the Word’s Hash Tag (e.g. Keyword) in the text.
Then we calculate the probability for E=S2.P+S3.P+…+Sp.P+1 where p=1,2,…(number of terms in Keyword). We use a kind of Deltas representation for the calculation.
We apply this method to all the fields.
NLP Description
Textual Keyword extraction – Description:
We use a Bayesian Adaptive Trigram Model for our Keyword extraction phase.
We use a probabilistic model which works on a triplet of words (E,T,P) and uses Keyword Frequencies (K.F.) for the calculation of the probabilities.
We apply an ad-hoc graph to represent our knowledge of words and their association.
First we train our Keyword model using a weight matrix W and a word vector V.
We have a weight matrix W (around 50000) representing the words’ importance based on their frequency (Keyword Frequencies) in a given corpus.
We construct V from S.V. = W*S where S is the frequency of the Word’s Hash Tag (e.g. Keyword) in the text.
Then we calculate the probability for E=S2.P+S3.P+…+Sp.P+1 where p=1,2,…(number of terms in Keyword). We use a kind of Deltas representation for the calculation.
We apply this method to all the fields.
Topic Modeling with Latent Dirichlet Allocation 384a16bd22

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