Timeline for How to overcome training example's different lengths when working with Word Embeddings (word2vec)
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 1, 2016 at 11:18 | comment | added | Hima Varsha | Have you tried using a vectorizer and doing fit_transform for the tweets? | |
| Aug 1, 2016 at 11:13 | comment | added | antorqs | I use all the word vectors. But I need to represent every tweet in a way they all have the same size. For the classifier, all of the examples must have the same size. | |
| Aug 1, 2016 at 11:11 | comment | added | Hima Varsha | I understand it a lot better after your edit. But why would want a single vector? why can't you use all the words(vectors) instead for classification? | |
| Aug 1, 2016 at 10:18 | comment | added | antorqs | Hello, thanks for your response. I updated the question with a small example. Would you update your answer to show how would you apply this to the example I provided? Thanks. | |
| Aug 1, 2016 at 10:11 | history | answered | Hima Varsha | CC BY-SA 3.0 |