unsubscribe Konstantinos Fotiadis Software Engineer Senior Innovation Technology Group Lockheed Martin IS&GS O: 610.354.7759 | M: 610.331.0013 -----Original Message----- From: Jörn Kottmann [mailto:[email protected]] Sent: Thursday, September 06, 2012 4:19 AM To: [email protected] Subject: EXTERNAL: Re: OpenNLP Maxent Data Format
Hello, SharpNLP is C# clone of OpenNLP, I never worked with it or know much about it, sorry. If you need to work with .NET and want to use OpenNLP, you can try this: https://cwiki.apache.org/OPENNLP/a-quick-guide-to-using-opennlp-from-net.html It should not be a problem to pass an non-existing feature to a model, in OpenNLP this is done all the time e.g. if there is word which was not seen in the training data before. HTH, Jörn On 09/04/2012 06:19 PM, David Young wrote: > Hi thanks for the reply. I am not as familiar with Java so I thought > Id produce a model first with SharpEntropy. > I have not really modified the simple example so It is only several > lines of basic code. > > This is how it works: > http://pastebin.com/LK9tNsrj > > The training data is as follows: > http://pastebin.com/3icni8Jc > > This example works fine but the problem is when I try to use any words > that are not in the training data. > For example > context.Add("oWord=someNewWord")... > > This gives an unknown key error because it is not recognised. But I > want to make predictions using what is known. The surrounding context. > > As a maximum entropy model I have lots of words in training data that > should be taken into account when available in addition to each word POS. > But sometimes in the real data I want to evaluate I have the POS for > each word, some words that are in the training data but also in the > context there might be words that are not in the training data. How do > I still get a prediction in this case using the rest of the context? > > Thanks for your time. > > On Tue, Sep 4, 2012 at 10:50 AM, Jörn Kottmann <[email protected]> wrote: > >> On 09/03/2012 01:45 AM, David Young wrote: >> >>> But my question is; what happens when I want to use something like >>> "next=WordNotInModel", a word that does not exist in the training >>> data, and still want to get a prediction using the rest of the >>> surrounding context? >>> Even If I use "next=Unknown" or "next=null" or Null I get an error >>> "predicateLabel KeyNotFoundException was unhandled". Because "next= >>> WordNotInModel" is not a known key. >>> >> Usually maxent is used as an API, can you post some code here so we >> can see what you are doing? Or do you use one of the command line >> utils? >> >> Thanks, >> Jörn >>
