Timeline for applying word2vec on small text files
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Apr 15, 2016 at 13:49 | comment | added | samsamara | thanks i'll have a look there. And reg the first point, even though word2vec processes the input sentence by sentence, the first word in sent 2 will look at 3 words from the end of sent 1 if the window size is 3, isn't it? I thought it considers each sentence as individual entities but im wrong i guess. | |
| Apr 15, 2016 at 9:40 | comment | added | chewpakabra | If you mix sentences, you basically will corrupt context information for last/first words in the sentence (since they will catch previous words as nearest, which is way not always true). Change wouldn't be total, but important, I assume. Considering w2v and d2v difference, I like the tutorial here: rare-technologies.com/doc2vec-tutorial . And basically, you are learning either an abstract vector representation of a word or a label assigned to sentence/document, so the core difference is the modelled object. | |
| Apr 15, 2016 at 9:31 | comment | added | samsamara | thanks for the input, makes more sense now. Also since word2vec processes the input sentence by sentence, what would happen if i mix up the sentences in the input document? that should totally change output vectors right? Also again, given it's processing sent by sent, how does word2vec differ from doc2vec? thanks again. | |
| Apr 15, 2016 at 8:33 | comment | added | chewpakabra | Probably, you are using too narrow context: if your model looks into, say, two words back and forward, you will be having up to 2 stopwords in context and that could give worse results. If you will broaden context (which will make model bigger and training time longer), with-stopwords model will give you better results, I assume. | |
| Apr 15, 2016 at 1:34 | comment | added | samsamara | but when i was using the distance tool to find most similar words to a given word, the version with stopwords removed gave me sensible words than the version without. can you guess what's this mean? | |
| Jan 11, 2016 at 17:09 | comment | added | chewpakabra | No, in the word2vec approach you don't need to do that, since the algorithm itself relies on a broad context to find similarities in words, so stop words (most of which are prepositions, pronouns and such) are an important asses for algorithm. | |
| Jan 11, 2016 at 12:38 | comment | added | samsamara | do we need to remove stopwords as a data pre processing step? | |
| Jan 11, 2016 at 10:26 | history | answered | chewpakabra | CC BY-SA 3.0 |