Timeline for Text-Classification-Problem: Is Word2Vec/NN the best approach?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Dec 5, 2015 at 20:50 | comment | added | wacax | You should check out sense2vec too arxiv.org/abs/1511.06388. In a nutshell it's word embeddings combined with Part-Of-Speech tagging. It's reported it made word embeddings more accurate by disambiguating homonyms. It would be interesting to see if it also improves performance in classification tasks. | |
| Nov 5, 2015 at 5:57 | comment | added | Shankar | Thanks @NBartley. Unseen words will also be a high probability. The input paras will be user generated content, hence the possibility of new unseen words will be very high. The categories would be defined, but we will need to expand the category list over time. Thanks | |
| Nov 4, 2015 at 19:27 | comment | added | rabbit | Can you clarify what kind of categories? Will it need to be able to handle new categories and/or unseen words? The requirements regarding infrequent terms and unseen categories will help the design of the system. | |
| Nov 4, 2015 at 16:45 | answer | added | rushimg | timeline score: 5 | |
| Nov 4, 2015 at 7:42 | review | First posts | |||
| Nov 4, 2015 at 8:04 | |||||
| Nov 4, 2015 at 7:34 | history | asked | Shankar | CC BY-SA 3.0 |