Thanks Samik for the suggestions.
#1: I think I should go with this one. I know about confusion matrix(matrix of
True positive, False positive and so on) but does openNLP provide any CLI or
API's for creating this confusion matrix or do you know any other tool/library
which I can use for this.
#2 Every time when I add some records or class in my corpus, I need to train it
from scratch.So, I don't think so there is a way to retrain the model again.
ThanksNikhil
From: Samik Raychaudhuri <sam...@gmail.com>
To: dev@opennlp.apache.org
Sent: Thursday, November 20, 2014 11:46 PM
Subject: Re: Need to speed up the model creation process of OpenNLP
Hi Nikhil,
#1: What I meant was: see if you can build a model on 1M records, check
the confusion matrix and see the performance. Then create a model on
1.5M records, check the confusion matrix and compare. If the improvement
is noticeable, then it would essentially make sense to train on more
data, on the other hand, if the improvement is not noticeable, then you
have already reached a plateau in terms of learning by the model. Please
look up confusion matrix related information on the web.
#2: Here the approach is somewhat different. If you have specific
classes of things that you need to identify, then start off with even
smaller data set containing training data related to one such class
(say, just 5K~10K set), then add training data incrementally from other
classes (and train again - from scratch). Note that, I do not think
there is a way to 'warm start' the learning: I do not think you can take
a model that has been trained on one class of data, and incrementally
make it learn on another set/class of data. That would be a nice
research problem. (BTW, if this is already possible, let me know).
Bottom line, if you have more data to train, it will take time. You can
consider some trade-offs in terms of ML as mentioned above. You should
definitely use the above along with parallelization, as mentioned by
Rodrigo/Joern - it would be a sin not to use it if you are on a
multi-core CPU. You might still need the 10gig java heap to process the
data though, IMHO.
HTH.
Best,
-Samik
On 19/11/2014 12:09 PM, nikhil jain wrote:
> Hi Samik,
> Thank you so much for the quick feedback.
> 1. You can possibly have smaller training sets and see if the models
> deteriorate substantially:
> Yes I have 4 training sets each containing 1 million records but i dont
> understand how it would be useful? because when I am creating a one model out
> of these 4 training sets then I have to pass all the records at once for
> creating a model so it would take time, right?
> 2. Another strategy is to incrementally introduce training sets containing
> specific class of Token Names - that would provide a quicker turnaroundRight,
> I am doing the same thing as you mentioned, like I have 4 different classes
> and each class contains 1 Million records. so initially I created a model on
> 1 Millions records so it took less time and worked properly then I added
> another one, so size of the corpus become 2 million and again created a model
> based on 2 million records and so on, but the problem is when i am adding
> more records in the corpus then model creation process is taking time.is it
> possible to reuse the model with new training set, means like i have a model
> based on 2 million records and now i can say reuse the old model but adjust
> the model again based on new records. if this is possible then small training
> sets would be useful, right?
> As I mentioned, I am new in openNLP and machine learning. so please explain
> with example if I am missing something.
>
> Thanks Nikhil
> From: Samik Raychaudhuri <sam...@gmail.com>
> To: dev@opennlp.apache.org
> Sent: Wednesday, November 19, 2014 6:00 AM
> Subject: Re: Need to speed up the model creation process of OpenNLP
>
> Hi,
> This is essentially a machine learning problem, nothing to do with
> OpenNLP. If you have such a large corpus, it would take a substantial
> amount of time to train models. You can possibly have smaller training
> sets and see if the models deteriorate substantially. Another strategy
> is to incrementally introduce training sets containing specific class of
> Token Names - that would provide a quicker turnaround.
> Hope this help.
> Best,
> -Samik
>
>
>
>
> On 18/11/2014 8:46 AM, nikhil jain wrote:
>> Hi,
>> I asked below question yesterday, did anyone get a chance to look at this.
>> I am new in OpenNLP and really need some help. Please provide some clue or
>> link or example.
>> ThanksNIkhil
>> From: nikhil jain <nikhil_jain1...@yahoo.com.INVALID>
>> To: "us...@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp
>>Apache <dev@opennlp.apache.org>
>> Sent: Tuesday, November 18, 2014 12:02 AM
>> Subject: Need to speed up the model creation process of OpenNLP
>>
>> Hi,
>> I am using OpenNLP Token Name Finder for parsing the unstructured data. I
>> have created a corpus of about 4 million records. When I am creating a model
>> out of the training set using openNLP API's in Eclipse using default setting
>> (cut-off 5 and iterations 100), process is taking a good amount of time,
>> around 2-3 hours.
>> Can someone suggest me how can I reduce the time as I want to experiment
>> with different iterations but as the model creation process is taking so
>> much time, I am not able to experiment with it. This is really a time
>> consuming process.
>> Please provide some feedback.
>> Thanks in advance.Nikhil Jain
>>
>>
>
>
>
