Usages of Adaptive features.

[rakeshbecse]

Please share the usages of Adaptive features that are used in NER tagging?

Regards,
Rakesh.P

Re: Performances of OpenNLP tools

[Jason Baldridge]

Jörn is absolutely right about that. Another good source of training data
is MASC. I've got some instructions for training models with MASC here:

https://github.com/scalanlp/chalk/wiki/Chalk-command-line-tutorial

Chalk (now defunct) provided a Scala wrapper around OpenNLP functionality,
so the instructions there should make it fairly straightforward to adapt
MASC data to OpenNLP.

-Jason

On Tue, 21 Jun 2016 at 10:46 Joern Kottmann  wrote:

> There are some research papers which study and compare the performance of
> NLP toolkits, but be careful often they don't train the NLP tools on the
> same data and the training data makes a big difference on the performance.
>
> Jörn
>
> On Tue, Jun 21, 2016 at 5:44 PM, Joern Kottmann 
> wrote:
>
> > Just don't use the very old existing models, to get good results you have
> > to train on your own data, especially if the domain of the data used for
> > training and the data which should be processed doesn't match. The old
> > models are trained on 90s news, those don't work well on todays news and
> > probably much worse on tweets.
> >
> > OntoNots is a good place to start if the goal is to process news. OpenNLP
> > comes with build-in support to train models from OntoNotes.
> >
> > Jörn
> >
> > On Tue, Jun 21, 2016 at 4:20 PM, Mattmann, Chris A (3980) <
> > chris.a.mattm...@jpl.nasa.gov> wrote:
> >
> >> This sounds like a fantastic idea.
> >>
> >> ++
> >> Chris Mattmann, Ph.D.
> >> Chief Architect
> >> Instrument Software and Science Data Systems Section (398)
> >> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> >> Office: 168-519, Mailstop: 168-527
> >> Email: chris.a.mattm...@nasa.gov
> >> WWW:  http://sunset.usc.edu/~mattmann/
> >> ++
> >> Director, Information Retrieval and Data Science Group (IRDS)
> >> Adjunct Associate Professor, Computer Science Department
> >> University of Southern California, Los Angeles, CA 90089 USA
> >> WWW: http://irds.usc.edu/
> >> ++
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >> On 6/21/16, 12:13 AM, "Anthony Beylerian"  >
> >> wrote:
> >>
> >> >+1
> >> >
> >> >Maybe we could put the results of the evaluator tests for each
> component
> >> somewhere on a webpage and on every release update them.
> >> >This is of course provided there are reasonable data sets for testing
> >> each component.
> >> >What do you think?
> >> >
> >> >Anthony
> >> >
> >> >> From: mondher.bouaz...@gmail.com
> >> >> Date: Tue, 21 Jun 2016 15:59:47 +0900
> >> >> Subject: Re: Performances of OpenNLP tools
> >> >> To: dev@opennlp.apache.org
> >> >>
> >> >> Hi,
> >> >>
> >> >> Thank you for your replies.
> >> >>
> >> >> Please Jeffrey accept once more my apologies for receiving the email
> >> twice.
> >> >>
> >> >> I also think it would be great to have such studies on the
> >> performances of
> >> >> OpenNLP.
> >> >>
> >> >> I have been looking for this information and checked in many places,
> >> >> including obviously google scholar, and I haven't found any serious
> >> studies
> >> >> or reliable results. Most of the existing ones report the
> performances
> >> of
> >> >> outdated releases of OpenNLP, and focus more on the execution time or
> >> >> CPU/RAM consumption, etc.
> >> >>
> >> >> I think such a comparison will help not only evaluate the overall
> >> accuracy,
> >> >> but also highlight the issues with the existing models (as a matter
> of
> >> >> fact, the existing models fail to recognize many of the hashtags in
> >> tweets:
> >> >> the tokenizer splits them into the "#" symbol and a word that the PoS
> >> >> tagger also fails to recognize).
> >> >>
> >> >> Therefore, building Twitter-based models would also be useful, since
> >> many
> >> >> of the works in academia / industry are focusing on Twitter data.
> >> >>
> >> >> Best regards,
> >> >>
> >> >> Mondher
> >> >>
> >> >>
> >> >>
> >> >> On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge <
> >> jasonbaldri...@gmail.com>
> >> >> wrote:
> >> >>
> >> >> > It would be fantastic to have these numbers. This is an example of
> >> >> > something that would be a great contribution by someone trying to
> >> >> > contribute to open source and who is maybe just getting into
> machine
> >> >> > learning and natural language processing.
> >> >> >
> >> >> > For Twitter-ish text, it'd be great to look at models trained and
> >> evaluated
> >> >> > on the Tweet NLP resources:
> >> >> >
> >> >> > http://www.cs.cmu.edu/~ark/TweetNLP/
> >> >> >
> >> >> > And comparing to how their models performed, etc. Also, it's worth
> >> looking
> >> >> > at spaCy (Python NLP modules) for further comparisons.
> >> >> >
> >> >> > https://spacy.io/
> >> >> >
> >> >> > -Jason
> >> >> >
> >> >> > On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick <
> 

Re: Performances of OpenNLP tools

[Joern Kottmann]

There are some research papers which study and compare the performance of
NLP toolkits, but be careful often they don't train the NLP tools on the
same data and the training data makes a big difference on the performance.

Jörn

On Tue, Jun 21, 2016 at 5:44 PM, Joern Kottmann  wrote:

> Just don't use the very old existing models, to get good results you have
> to train on your own data, especially if the domain of the data used for
> training and the data which should be processed doesn't match. The old
> models are trained on 90s news, those don't work well on todays news and
> probably much worse on tweets.
>
> OntoNots is a good place to start if the goal is to process news. OpenNLP
> comes with build-in support to train models from OntoNotes.
>
> Jörn
>
> On Tue, Jun 21, 2016 at 4:20 PM, Mattmann, Chris A (3980) <
> chris.a.mattm...@jpl.nasa.gov> wrote:
>
>> This sounds like a fantastic idea.
>>
>> ++
>> Chris Mattmann, Ph.D.
>> Chief Architect
>> Instrument Software and Science Data Systems Section (398)
>> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
>> Office: 168-519, Mailstop: 168-527
>> Email: chris.a.mattm...@nasa.gov
>> WWW:  http://sunset.usc.edu/~mattmann/
>> ++
>> Director, Information Retrieval and Data Science Group (IRDS)
>> Adjunct Associate Professor, Computer Science Department
>> University of Southern California, Los Angeles, CA 90089 USA
>> WWW: http://irds.usc.edu/
>> ++
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> On 6/21/16, 12:13 AM, "Anthony Beylerian" 
>> wrote:
>>
>> >+1
>> >
>> >Maybe we could put the results of the evaluator tests for each component
>> somewhere on a webpage and on every release update them.
>> >This is of course provided there are reasonable data sets for testing
>> each component.
>> >What do you think?
>> >
>> >Anthony
>> >
>> >> From: mondher.bouaz...@gmail.com
>> >> Date: Tue, 21 Jun 2016 15:59:47 +0900
>> >> Subject: Re: Performances of OpenNLP tools
>> >> To: dev@opennlp.apache.org
>> >>
>> >> Hi,
>> >>
>> >> Thank you for your replies.
>> >>
>> >> Please Jeffrey accept once more my apologies for receiving the email
>> twice.
>> >>
>> >> I also think it would be great to have such studies on the
>> performances of
>> >> OpenNLP.
>> >>
>> >> I have been looking for this information and checked in many places,
>> >> including obviously google scholar, and I haven't found any serious
>> studies
>> >> or reliable results. Most of the existing ones report the performances
>> of
>> >> outdated releases of OpenNLP, and focus more on the execution time or
>> >> CPU/RAM consumption, etc.
>> >>
>> >> I think such a comparison will help not only evaluate the overall
>> accuracy,
>> >> but also highlight the issues with the existing models (as a matter of
>> >> fact, the existing models fail to recognize many of the hashtags in
>> tweets:
>> >> the tokenizer splits them into the "#" symbol and a word that the PoS
>> >> tagger also fails to recognize).
>> >>
>> >> Therefore, building Twitter-based models would also be useful, since
>> many
>> >> of the works in academia / industry are focusing on Twitter data.
>> >>
>> >> Best regards,
>> >>
>> >> Mondher
>> >>
>> >>
>> >>
>> >> On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge <
>> jasonbaldri...@gmail.com>
>> >> wrote:
>> >>
>> >> > It would be fantastic to have these numbers. This is an example of
>> >> > something that would be a great contribution by someone trying to
>> >> > contribute to open source and who is maybe just getting into machine
>> >> > learning and natural language processing.
>> >> >
>> >> > For Twitter-ish text, it'd be great to look at models trained and
>> evaluated
>> >> > on the Tweet NLP resources:
>> >> >
>> >> > http://www.cs.cmu.edu/~ark/TweetNLP/
>> >> >
>> >> > And comparing to how their models performed, etc. Also, it's worth
>> looking
>> >> > at spaCy (Python NLP modules) for further comparisons.
>> >> >
>> >> > https://spacy.io/
>> >> >
>> >> > -Jason
>> >> >
>> >> > On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick 
>> >> > wrote:
>> >> >
>> >> > > I saw the same question on the users list on June 17. At least I
>> thought
>> >> > it
>> >> > > was the same question -- sorry if it wasn't.
>> >> > >
>> >> > > On Mon, Jun 20, 2016 at 11:37 AM, Mattmann, Chris A (3980) <
>> >> > > chris.a.mattm...@jpl.nasa.gov> wrote:
>> >> > >
>> >> > > > Well, hold on. He sent that mail (as of the time of this mail) 4
>> >> > > > mins previously. Maybe some folks need some time to reply ^_^
>> >> > > >
>> >> > > >
>> ++
>> >> > > > Chris Mattmann, Ph.D.
>> >> > > > Chief Architect
>> >> > > > Instrument Software and Science Data Systems Section (398)
>> >> > > > NASA Jet 

Re: Performances of OpenNLP tools

[Joern Kottmann]

Just don't use the very old existing models, to get good results you have
to train on your own data, especially if the domain of the data used for
training and the data which should be processed doesn't match. The old
models are trained on 90s news, those don't work well on todays news and
probably much worse on tweets.

OntoNots is a good place to start if the goal is to process news. OpenNLP
comes with build-in support to train models from OntoNotes.

Jörn

On Tue, Jun 21, 2016 at 4:20 PM, Mattmann, Chris A (3980) <
chris.a.mattm...@jpl.nasa.gov> wrote:

> This sounds like a fantastic idea.
>
> ++
> Chris Mattmann, Ph.D.
> Chief Architect
> Instrument Software and Science Data Systems Section (398)
> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> Office: 168-519, Mailstop: 168-527
> Email: chris.a.mattm...@nasa.gov
> WWW:  http://sunset.usc.edu/~mattmann/
> ++
> Director, Information Retrieval and Data Science Group (IRDS)
> Adjunct Associate Professor, Computer Science Department
> University of Southern California, Los Angeles, CA 90089 USA
> WWW: http://irds.usc.edu/
> ++
>
>
>
>
>
>
>
>
>
>
> On 6/21/16, 12:13 AM, "Anthony Beylerian" 
> wrote:
>
> >+1
> >
> >Maybe we could put the results of the evaluator tests for each component
> somewhere on a webpage and on every release update them.
> >This is of course provided there are reasonable data sets for testing
> each component.
> >What do you think?
> >
> >Anthony
> >
> >> From: mondher.bouaz...@gmail.com
> >> Date: Tue, 21 Jun 2016 15:59:47 +0900
> >> Subject: Re: Performances of OpenNLP tools
> >> To: dev@opennlp.apache.org
> >>
> >> Hi,
> >>
> >> Thank you for your replies.
> >>
> >> Please Jeffrey accept once more my apologies for receiving the email
> twice.
> >>
> >> I also think it would be great to have such studies on the performances
> of
> >> OpenNLP.
> >>
> >> I have been looking for this information and checked in many places,
> >> including obviously google scholar, and I haven't found any serious
> studies
> >> or reliable results. Most of the existing ones report the performances
> of
> >> outdated releases of OpenNLP, and focus more on the execution time or
> >> CPU/RAM consumption, etc.
> >>
> >> I think such a comparison will help not only evaluate the overall
> accuracy,
> >> but also highlight the issues with the existing models (as a matter of
> >> fact, the existing models fail to recognize many of the hashtags in
> tweets:
> >> the tokenizer splits them into the "#" symbol and a word that the PoS
> >> tagger also fails to recognize).
> >>
> >> Therefore, building Twitter-based models would also be useful, since
> many
> >> of the works in academia / industry are focusing on Twitter data.
> >>
> >> Best regards,
> >>
> >> Mondher
> >>
> >>
> >>
> >> On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge <
> jasonbaldri...@gmail.com>
> >> wrote:
> >>
> >> > It would be fantastic to have these numbers. This is an example of
> >> > something that would be a great contribution by someone trying to
> >> > contribute to open source and who is maybe just getting into machine
> >> > learning and natural language processing.
> >> >
> >> > For Twitter-ish text, it'd be great to look at models trained and
> evaluated
> >> > on the Tweet NLP resources:
> >> >
> >> > http://www.cs.cmu.edu/~ark/TweetNLP/
> >> >
> >> > And comparing to how their models performed, etc. Also, it's worth
> looking
> >> > at spaCy (Python NLP modules) for further comparisons.
> >> >
> >> > https://spacy.io/
> >> >
> >> > -Jason
> >> >
> >> > On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick 
> >> > wrote:
> >> >
> >> > > I saw the same question on the users list on June 17. At least I
> thought
> >> > it
> >> > > was the same question -- sorry if it wasn't.
> >> > >
> >> > > On Mon, Jun 20, 2016 at 11:37 AM, Mattmann, Chris A (3980) <
> >> > > chris.a.mattm...@jpl.nasa.gov> wrote:
> >> > >
> >> > > > Well, hold on. He sent that mail (as of the time of this mail) 4
> >> > > > mins previously. Maybe some folks need some time to reply ^_^
> >> > > >
> >> > > > ++
> >> > > > Chris Mattmann, Ph.D.
> >> > > > Chief Architect
> >> > > > Instrument Software and Science Data Systems Section (398)
> >> > > > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> >> > > > Office: 168-519, Mailstop: 168-527
> >> > > > Email: chris.a.mattm...@nasa.gov
> >> > > > WWW:  http://sunset.usc.edu/~mattmann/
> >> > > > ++
> >> > > > Director, Information Retrieval and Data Science Group (IRDS)
> >> > > > Adjunct Associate Professor, Computer Science Department
> >> > > > University of Southern California, Los Angeles, 

Re: Performances of OpenNLP tools

[Mattmann, Chris A (3980)]

This sounds like a fantastic idea.

++
Chris Mattmann, Ph.D.
Chief Architect
Instrument Software and Science Data Systems Section (398)
NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
Office: 168-519, Mailstop: 168-527
Email: chris.a.mattm...@nasa.gov
WWW:  http://sunset.usc.edu/~mattmann/
++
Director, Information Retrieval and Data Science Group (IRDS)
Adjunct Associate Professor, Computer Science Department
University of Southern California, Los Angeles, CA 90089 USA
WWW: http://irds.usc.edu/
++










On 6/21/16, 12:13 AM, "Anthony Beylerian"  wrote:

>+1 
>
>Maybe we could put the results of the evaluator tests for each component 
>somewhere on a webpage and on every release update them.
>This is of course provided there are reasonable data sets for testing each 
>component.
>What do you think?
>
>Anthony
>
>> From: mondher.bouaz...@gmail.com
>> Date: Tue, 21 Jun 2016 15:59:47 +0900
>> Subject: Re: Performances of OpenNLP tools
>> To: dev@opennlp.apache.org
>> 
>> Hi,
>> 
>> Thank you for your replies.
>> 
>> Please Jeffrey accept once more my apologies for receiving the email twice.
>> 
>> I also think it would be great to have such studies on the performances of
>> OpenNLP.
>> 
>> I have been looking for this information and checked in many places,
>> including obviously google scholar, and I haven't found any serious studies
>> or reliable results. Most of the existing ones report the performances of
>> outdated releases of OpenNLP, and focus more on the execution time or
>> CPU/RAM consumption, etc.
>> 
>> I think such a comparison will help not only evaluate the overall accuracy,
>> but also highlight the issues with the existing models (as a matter of
>> fact, the existing models fail to recognize many of the hashtags in tweets:
>> the tokenizer splits them into the "#" symbol and a word that the PoS
>> tagger also fails to recognize).
>> 
>> Therefore, building Twitter-based models would also be useful, since many
>> of the works in academia / industry are focusing on Twitter data.
>> 
>> Best regards,
>> 
>> Mondher
>> 
>> 
>> 
>> On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge 
>> wrote:
>> 
>> > It would be fantastic to have these numbers. This is an example of
>> > something that would be a great contribution by someone trying to
>> > contribute to open source and who is maybe just getting into machine
>> > learning and natural language processing.
>> >
>> > For Twitter-ish text, it'd be great to look at models trained and evaluated
>> > on the Tweet NLP resources:
>> >
>> > http://www.cs.cmu.edu/~ark/TweetNLP/
>> >
>> > And comparing to how their models performed, etc. Also, it's worth looking
>> > at spaCy (Python NLP modules) for further comparisons.
>> >
>> > https://spacy.io/
>> >
>> > -Jason
>> >
>> > On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick 
>> > wrote:
>> >
>> > > I saw the same question on the users list on June 17. At least I thought
>> > it
>> > > was the same question -- sorry if it wasn't.
>> > >
>> > > On Mon, Jun 20, 2016 at 11:37 AM, Mattmann, Chris A (3980) <
>> > > chris.a.mattm...@jpl.nasa.gov> wrote:
>> > >
>> > > > Well, hold on. He sent that mail (as of the time of this mail) 4
>> > > > mins previously. Maybe some folks need some time to reply ^_^
>> > > >
>> > > > ++
>> > > > Chris Mattmann, Ph.D.
>> > > > Chief Architect
>> > > > Instrument Software and Science Data Systems Section (398)
>> > > > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
>> > > > Office: 168-519, Mailstop: 168-527
>> > > > Email: chris.a.mattm...@nasa.gov
>> > > > WWW:  http://sunset.usc.edu/~mattmann/
>> > > > ++
>> > > > Director, Information Retrieval and Data Science Group (IRDS)
>> > > > Adjunct Associate Professor, Computer Science Department
>> > > > University of Southern California, Los Angeles, CA 90089 USA
>> > > > WWW: http://irds.usc.edu/
>> > > > ++
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > > On 6/20/16, 8:23 AM, "Jeffrey Zemerick"  wrote:
>> > > >
>> > > > >Hi Mondher,
>> > > > >
>> > > > >Since you didn't get any replies I'm guessing no one is aware of any
>> > > > >resources related to what you need. Google Scholar is a good place to
>> > > look
>> > > > >for papers referencing OpenNLP and its methods (in case you haven't
>> > > > >searched it already).
>> > > > >
>> > > > >Jeff
>> > > > >
>> > > > >On Mon, Jun 20, 2016 at 11:19 AM, Mondher Bouazizi <
>> > > > >mondher.bouaz...@gmail.com> wrote:
>> > > > >
>> > > > >> Hi,
>> > 

RE: Performances of OpenNLP tools

[Anthony Beylerian]

+1 

Maybe we could put the results of the evaluator tests for each component 
somewhere on a webpage and on every release update them.
This is of course provided there are reasonable data sets for testing each 
component.
What do you think?

Anthony

> From: mondher.bouaz...@gmail.com
> Date: Tue, 21 Jun 2016 15:59:47 +0900
> Subject: Re: Performances of OpenNLP tools
> To: dev@opennlp.apache.org
> 
> Hi,
> 
> Thank you for your replies.
> 
> Please Jeffrey accept once more my apologies for receiving the email twice.
> 
> I also think it would be great to have such studies on the performances of
> OpenNLP.
> 
> I have been looking for this information and checked in many places,
> including obviously google scholar, and I haven't found any serious studies
> or reliable results. Most of the existing ones report the performances of
> outdated releases of OpenNLP, and focus more on the execution time or
> CPU/RAM consumption, etc.
> 
> I think such a comparison will help not only evaluate the overall accuracy,
> but also highlight the issues with the existing models (as a matter of
> fact, the existing models fail to recognize many of the hashtags in tweets:
> the tokenizer splits them into the "#" symbol and a word that the PoS
> tagger also fails to recognize).
> 
> Therefore, building Twitter-based models would also be useful, since many
> of the works in academia / industry are focusing on Twitter data.
> 
> Best regards,
> 
> Mondher
> 
> 
> 
> On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge 
> wrote:
> 
> > It would be fantastic to have these numbers. This is an example of
> > something that would be a great contribution by someone trying to
> > contribute to open source and who is maybe just getting into machine
> > learning and natural language processing.
> >
> > For Twitter-ish text, it'd be great to look at models trained and evaluated
> > on the Tweet NLP resources:
> >
> > http://www.cs.cmu.edu/~ark/TweetNLP/
> >
> > And comparing to how their models performed, etc. Also, it's worth looking
> > at spaCy (Python NLP modules) for further comparisons.
> >
> > https://spacy.io/
> >
> > -Jason
> >
> > On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick 
> > wrote:
> >
> > > I saw the same question on the users list on June 17. At least I thought
> > it
> > > was the same question -- sorry if it wasn't.
> > >
> > > On Mon, Jun 20, 2016 at 11:37 AM, Mattmann, Chris A (3980) <
> > > chris.a.mattm...@jpl.nasa.gov> wrote:
> > >
> > > > Well, hold on. He sent that mail (as of the time of this mail) 4
> > > > mins previously. Maybe some folks need some time to reply ^_^
> > > >
> > > > ++
> > > > Chris Mattmann, Ph.D.
> > > > Chief Architect
> > > > Instrument Software and Science Data Systems Section (398)
> > > > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> > > > Office: 168-519, Mailstop: 168-527
> > > > Email: chris.a.mattm...@nasa.gov
> > > > WWW:  http://sunset.usc.edu/~mattmann/
> > > > ++
> > > > Director, Information Retrieval and Data Science Group (IRDS)
> > > > Adjunct Associate Professor, Computer Science Department
> > > > University of Southern California, Los Angeles, CA 90089 USA
> > > > WWW: http://irds.usc.edu/
> > > > ++
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > On 6/20/16, 8:23 AM, "Jeffrey Zemerick"  wrote:
> > > >
> > > > >Hi Mondher,
> > > > >
> > > > >Since you didn't get any replies I'm guessing no one is aware of any
> > > > >resources related to what you need. Google Scholar is a good place to
> > > look
> > > > >for papers referencing OpenNLP and its methods (in case you haven't
> > > > >searched it already).
> > > > >
> > > > >Jeff
> > > > >
> > > > >On Mon, Jun 20, 2016 at 11:19 AM, Mondher Bouazizi <
> > > > >mondher.bouaz...@gmail.com> wrote:
> > > > >
> > > > >> Hi,
> > > > >>
> > > > >> Apologies if you received multiple copies of this email. I sent it
> > to
> > > > the
> > > > >> users list a while ago, and haven't had an answer yet.
> > > > >>
> > > > >> I have been looking for a while if there is any relevant work that
> > > > >> performed tests on the OpenNLP tools (in particular the Lemmatizer,
> > > > >> Tokenizer and PoS-Tagger) when used with short and noisy texts such
> > as
> > > > >> Twitter data, etc., and/or compared it to other libraries.
> > > > >>
> > > > >> By performances, I mean accuracy/precision, rather than time of
> > > > execution,
> > > > >> etc.
> > > > >>
> > > > >> If anyone can refer me to a paper or a work done in this context,
> > that
> > > > >> would be of great help.
> > > > >>
> > > > >> Thank you very much.
> > > > >>
> > > > >> Mondher
> > > > >>
> > > >
> > >
> >
  

Re: Performances of OpenNLP tools

[Mondher Bouazizi]

Hi,

Thank you for your replies.

Please Jeffrey accept once more my apologies for receiving the email twice.

I also think it would be great to have such studies on the performances of
OpenNLP.

I have been looking for this information and checked in many places,
including obviously google scholar, and I haven't found any serious studies
or reliable results. Most of the existing ones report the performances of
outdated releases of OpenNLP, and focus more on the execution time or
CPU/RAM consumption, etc.

I think such a comparison will help not only evaluate the overall accuracy,
but also highlight the issues with the existing models (as a matter of
fact, the existing models fail to recognize many of the hashtags in tweets:
the tokenizer splits them into the "#" symbol and a word that the PoS
tagger also fails to recognize).

Therefore, building Twitter-based models would also be useful, since many
of the works in academia / industry are focusing on Twitter data.

Best regards,

Mondher



On Tue, Jun 21, 2016 at 12:45 AM, Jason Baldridge 
wrote:

> It would be fantastic to have these numbers. This is an example of
> something that would be a great contribution by someone trying to
> contribute to open source and who is maybe just getting into machine
> learning and natural language processing.
>
> For Twitter-ish text, it'd be great to look at models trained and evaluated
> on the Tweet NLP resources:
>
> http://www.cs.cmu.edu/~ark/TweetNLP/
>
> And comparing to how their models performed, etc. Also, it's worth looking
> at spaCy (Python NLP modules) for further comparisons.
>
> https://spacy.io/
>
> -Jason
>
> On Mon, 20 Jun 2016 at 10:41 Jeffrey Zemerick 
> wrote:
>
> > I saw the same question on the users list on June 17. At least I thought
> it
> > was the same question -- sorry if it wasn't.
> >
> > On Mon, Jun 20, 2016 at 11:37 AM, Mattmann, Chris A (3980) <
> > chris.a.mattm...@jpl.nasa.gov> wrote:
> >
> > > Well, hold on. He sent that mail (as of the time of this mail) 4
> > > mins previously. Maybe some folks need some time to reply ^_^
> > >
> > > ++
> > > Chris Mattmann, Ph.D.
> > > Chief Architect
> > > Instrument Software and Science Data Systems Section (398)
> > > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> > > Office: 168-519, Mailstop: 168-527
> > > Email: chris.a.mattm...@nasa.gov
> > > WWW:  http://sunset.usc.edu/~mattmann/
> > > ++
> > > Director, Information Retrieval and Data Science Group (IRDS)
> > > Adjunct Associate Professor, Computer Science Department
> > > University of Southern California, Los Angeles, CA 90089 USA
> > > WWW: http://irds.usc.edu/
> > > ++
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > > On 6/20/16, 8:23 AM, "Jeffrey Zemerick"  wrote:
> > >
> > > >Hi Mondher,
> > > >
> > > >Since you didn't get any replies I'm guessing no one is aware of any
> > > >resources related to what you need. Google Scholar is a good place to
> > look
> > > >for papers referencing OpenNLP and its methods (in case you haven't
> > > >searched it already).
> > > >
> > > >Jeff
> > > >
> > > >On Mon, Jun 20, 2016 at 11:19 AM, Mondher Bouazizi <
> > > >mondher.bouaz...@gmail.com> wrote:
> > > >
> > > >> Hi,
> > > >>
> > > >> Apologies if you received multiple copies of this email. I sent it
> to
> > > the
> > > >> users list a while ago, and haven't had an answer yet.
> > > >>
> > > >> I have been looking for a while if there is any relevant work that
> > > >> performed tests on the OpenNLP tools (in particular the Lemmatizer,
> > > >> Tokenizer and PoS-Tagger) when used with short and noisy texts such
> as
> > > >> Twitter data, etc., and/or compared it to other libraries.
> > > >>
> > > >> By performances, I mean accuracy/precision, rather than time of
> > > execution,
> > > >> etc.
> > > >>
> > > >> If anyone can refer me to a paper or a work done in this context,
> that
> > > >> would be of great help.
> > > >>
> > > >> Thank you very much.
> > > >>
> > > >> Mondher
> > > >>
> > >
> >
>