Hidden Markov models package in R - r

Question

I need some help implementing a HMM module in R. I'm new to R and don't have a lot of knowledge on it.
So i have to implement an IE using HMM, i have 2 folders with files, one with the sentences and the other with the corresponding tags i want to learn form each sentence.
folder1 > event1.txt: "2013 2nd International Conference on Information and Knowledge Management (ICIKM 2013) will be held in Chengdu, China during July 20-21, 2013."
folder2 > event1.txt:
"N: 2nd International Conference on Information and Knowledge Management (ICIKM 2013)
D: July 20-21, 2013
L: Chengdu, China"
N -> Name; D -> Date; L -> Location
My question is how to implement it on R, how do i initialize the model and how do i do to train it? And then how do i apply it to a random sentence to extract the information?
Thanks in advance for all the help!

Answer 1

If you run the following command:
RSiteSearch('hidden markov model')
Then it finds 4 Task Views, 40 Vignettes, and 255 functions (when I ran it, there could be more by the time you run it).
I would suggest looking through those results (probably start with the views and vignettes) to see if anything there works for you. If not, then tell us what you have tried and what you need that is not provided there.

Answer 2

I'm not sure what exactly you want to do, but you might find this excellent tutorial on hidden Markov models using R useful. You build the functions and Markov models from scratch starting from regular Markov models and then moving to hidden Markov models. That's really valuable to understand how they work.
There is also the R package depmixS4 for specifying and fitting hidden Markov models. It's documentation is pretty solid and going through the example code might help you.

Answer 3

depmixS4 is most general and reasonably good package, if you get it to work on your data. It checked out on dummy data for me but gave error on real data. HMM also works but only if you have discrete variables and not continuous.

Answer 4

DepmixS4 is what you are looking for.
First of all, you need to identify best number of hidden states for your model. This can be done by taking model with least value of AIC for different hidden states.
I have created a function HMM_model_execution which will return the model variable and number of states for the model.
library(depmixS4)
first column should be visible state and remaining external variable in doc_data
HMM_model_execution<-function( doc_data, k)
k number of total hidden state to compare
{
aic_values <- vector(mode="numeric", length=k-1) # to store AIC values
for( i in 2:k)
{
print(paste("loop counter",i))
mod <- depmix(response = doc_data$numpresc ~ 1, data = doc_data, nstates = i)
fm <- fit(mod, verbose = FALSE)
aic_values[i-1]<- AIC(fm)
#print(paste("Aic value at this index",aic_values[i-1]))
#writeLines("\n")
}
min_index<-which.min(aic_values)
no of hidden states for best model
#print(paste("index of minimum AIC",min_index))
mod <- depmix(response = doc_data$numpresc ~ 1, data = doc_data, nstates = (min_index+1))
fm <- fit(mod, verbose = FALSE)
best model execution
print(paste("best model with number of hidden states", min_index+1))
return(c(fm, min_index+1))
writeLines("\n")
writeLines("\n")
External variables( co-variates can be passed in function depmix ). summary (fm) will give you all model parameters.