Hi Boris,

Thank you very much for sharing your experience with us!
Is it possible to ask you for more information?

I have only just recently used d4lj with some introductory material,
however I have also felt doc2vec could also be quite useful, although my
understanding of it is still limited.

My current understanding is that doc2vec as an extension of word2vec, can
capture a more generalized context (the document) instead of just focusing
on the context of a single word, in order to provide features useful to
classify that document.

The advantage would be to better capture latent information that exist in
the document (such as the order of words), instead of just averaging word
vectors, or through other approaches on the document level (would love some
feedback on this)

The generalizations could hurt the classification performance in some
tasks, but seem to be more useful when the target documents are larger.

It could also be possible to chose the "document" to be a single word as
well, reducing the underlying matrix to an array, does that make sense?

Therefore, we could also use document based vectors for mid to high-layer
tasks (doc cat, sentiment, profile etc..). What do you think?

It would be fantastic to clarify, I believe that would also motivate more
people to pitch in and better assist with this.

Thanks,

Anthony
Hi William


I have never heard of Features2Vec.

I think for low-level tasks, pre-linguistic tasks such as text
classification where we don't want to build models and have a one-fits-all
solution, Word2Vec works well. I used it in industrial environment for text
classification, some information extraction and content generation tasks.
So I think it should also work for low-level OpenNLP tasks.


Regards

Boris


________________________________
From: William Colen <william.co...@gmail.com>
Sent: Wednesday, June 29, 2016 4:43:25 AM
To: dev@opennlp.apache.org
Subject: Re: DeepLearning4J as a ML for OpenNLP

Thank you, Boris. I am new to DeepLearning, so I have no idea the issues we
would face. I was wondering if we can use Features2Vec instead of Word2Vec,
does it make any sense?
The idea was to use DL in low level NLP tasks where we don't have parse
trees yet.


2016-06-29 6:34 GMT-03:00 Boris Galitsky <bgalit...@hotmail.com>:

> Hi guys
>
>   I should mention how we used DeepLearning4J for the OpenNLP.Similarity
> project at
>
> https://github.com/bgalitsky/relevance-based-on-parse-trees
>
>
> The main question is how word2vec models and linguistic information such
> as part trees complement each other. In a word2vec approach any two words
> can be compared. The weakness here is that when learning is based on
> computing a distance between totally unrelated words like 'cat' and 'fly'
> can be meaningless, uninformative and can corrupt a learning model.
>
>
> In OpenNLP.Similarity component similarity is defined  in terms of parse
> trees. When word2vec is applied on top of parse trees and not as a
> bag-of-words, we only compute the distance between the words with the same
> semantic role, so the model becomes more accurate.
>
>
> There's a paper on the way which does the assessment of relevance
> improvent for
>
>
> word2vec (bag-of-words) [traditional] vs word2vec (parse-trees)
>
>
> Regards
>
> Boris
>
> [https://avatars3.githubusercontent.com/u/1051120?v=3&s=400]<
> https://github.com/bgalitsky/relevance-based-on-parse-trees>
>
> bgalitsky/relevance-based-on-parse-trees<
> https://github.com/bgalitsky/relevance-based-on-parse-trees>
> github.com
> Automatically exported from
> code.google.com/p/relevance-based-on-parse-trees
>
>
>
>
> ________________________________
> From: Anthony Beylerian <anthony.beyler...@gmail.com>
> Sent: Wednesday, June 29, 2016 2:13:38 AM
> To: dev@opennlp.apache.org
> Subject: Re: DeepLearning4J as a ML for OpenNLP
>
> +1 would be willing to help out when possible
>

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