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    phalexo
    @phalexo
    This is before your tweaking vectors?
    Evgeny Denisov
    @eIGato
    No. I do discard the bulk-trained vectors.
    This is similarity between first inferrence (steps = d2v.iter ** 2, alpha = min_alpha = d2v.alpha) and the last result from interactive shell.
    Evgeny Denisov
    @eIGato
    F@#$%! All that time i did infer documents from a generator. One-time generator. They did not re-infer at all. Bleen.
    Evgeny Denisov
    @eIGato
    image.png
    This is similarity between first inferrence and alpha = min_alpha = 0.19, steps = 4500.
    Evgeny Denisov
    @eIGato
    Replaced document vectors with copy of inferred ones. And re-inferred. Similarity is about 1.0 like before. Because infer_vector() doesn't use old docvecs at all.
    But i still don't know if it makes sense.
    phalexo
    @phalexo
    I don't either. Makes no sense to me. Considering that inferred vectors should be different based on parameters, it would seem odd.
    Evgeny Denisov
    @eIGato
    d2v.sample = 1.0 / word_count
    Is it reasonable?
    Evgeny Denisov
    @eIGato
    word_count is the count of different words (provisional length of d2v.wv.index2word).
    Evgeny Denisov
    @eIGato
    How to pick a reasonable sample value?
    Evgeny Denisov
    @eIGato
    What if 90% of words are different from each other? Is it possible to train *2Vec model with such a corpus?
    phalexo
    @phalexo
    Clearly that is not a natural language application.
    Evgeny Denisov
    @eIGato
    @phalexo purpose is to predict phrases, not words. So i use phrases as d2v words. And full texts as d2v docs.
    Saurabh Vyas
    @saurabhvyas
    is there a pretrained lda model available for gensim , just for tinkering ?
    Matan Shenhav
    @ixxie
    @saurabhvyas can LDA even be used in a supervised mode?
    anyway, its been pretty easy for us to train+predict on a given data set
    Dennis.Chen
    @DennisChen0307
    Hi there. Is there any road maps for new release of gensim?
    AMaini503
    @AMaini503
    Should I expect Doc2Vec to use all the cores if I pass workers = #cpus ?
    matanster
    @matanster
    Apologies for adding a 4th question in a row here...
    Does gensim have anything builtin for transforming a document to a bag of n-grams representation, or does it in fact only do bag of words? (words being 1-grams...)
    Radim Řehůřek
    @piskvorky
    @matanster Gensim doesn't actually do the transformation; it already expects a (feature_id, weight) bag-of-whatever pairs on input. How you split the documents into words/ngrams/something else is up to you.
    @DennisChen0307 our aim is one release per month, but last months have been busy at RARE, not much time for open source. We plan a release for the end of this month.
    matanster
    @matanster
    @piskvorky oh, sorry then, I just thought maybe corpora.Dictionary.doc2bow might have some usage form for that... I could swear I saw it computing the bag-of-words in my code, but I should probably start reading the source to answer my own questions
    Jesse Talavera-Greenberg
    @JesseTG
    When training a word2vec model, I need to give it a list of documents. How does word2vec treat unknown words? By giving an unknown word a vector close to a known word?
    phalexo
    @phalexo
    It does the initial pass, compiling a corpus dictionary. If it does not make into the dictionary, I believe, it is totally ignored thereafter.
    Jesse Talavera-Greenberg
    @JesseTG
    @phalexo So wait, how can I use word2vec to analyze Tweets if slang and trending topics are always changing? Or am I missing something?
    phalexo
    @phalexo
    You would have to continue training. Maybe there is a way to update the dictionary.
    In any case, with Twitter you have a huge problem because people abbreviate everything, make up their own words, etc...
    Jesse Talavera-Greenberg
    @JesseTG
    @phalexo What would you suggest?
    phalexo
    @phalexo
    This sounds like the problem posed on experfy.com site.
    Jesse Talavera-Greenberg
    @JesseTG
    What do you mean?
    phalexo
    @phalexo
    Well, you have to experiment. Maybe there is a large repository of tweets. I'd train on that.
    Jesse Talavera-Greenberg
    @JesseTG
    I have lots of data already. Like, terabytes. My problem is deciding what to do with it.
    phalexo
    @phalexo
    There was a project posted in which the wanted to track groups of people by language they use (slang)
    Jesse Talavera-Greenberg
    @JesseTG
    Specifically, I'm trying to detect whether or not a given user is a sock puppet or bot (because Russia screwing with our elections pisses me off)
    I have a list of known bots and I'm currently combing through my data to get some (but not all!) of the tweets made by these bots
    phalexo
    @phalexo
    Well, train the general corpus first.
    including the bots and known bad actors.
    Jesse Talavera-Greenberg
    @JesseTG
    Here's a catch; I also want to consider URLs and usernames that these bots commonly post. Should I consider those to be words?
    Also, not all bots will have the same amount of tweets available. For some bots I might have hundreds, for others I might have tens. I don't know yet, the job is still running
    phalexo
    @phalexo
    mark every tweet with a tag "bad dude" "maybe not bad"
    Jesse Talavera-Greenberg
    @JesseTG
    Technically I'm evaluating users, not tweets
    phalexo
    @phalexo
    Ignore URLs too brittle.
    Jesse Talavera-Greenberg
    @JesseTG
    How so?
    phalexo
    @phalexo
    Strip URLs out, junk stuff. Just wastes time and space.
    Jesse Talavera-Greenberg
    @JesseTG
    But part of the process of spreading misinformation is posting links...
    phalexo
    @phalexo
    URLs will change all the time, they tell you nothing.
    And URLs have no natural location within English.
    it is junk.