Closed eveliao closed 5 years ago
Hi Eveliao
That's correct, I used in SemEval2018, might have training logs somewhere, will get back to you if I find those. These might help you with exact training strategy/hyper-params. For the performance evaluation on SemEval2018 Task 7 see: Talla at SemEval-2018 Task 7
Regarding, i1 = newseq_words.index('entity_1_end')
It is just taking the last(right-most) word of a multi-word entity. While in original paper, authors used the head-word of a multi-word entity. There is also one crucial difference from the original paper, this implementation does not implement Special Treatment of Artificial Classes
(as described in the original paper).
Thanks!
Hi, I tried to run your model, I get this error, OSError: [E050] Can't find model 'en'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory. Do we have a directory with the name of "models". As it was not on your repository.
Hi It looks like Spacy error. Check this out and make sure you download spacy's model: https://github.com/explosion/spaCy/issues/1721
Hi @eveliao Sorry, I couldn't find the original training logs. I hope you got it working. Thanks!
I noticed that in
dataio.py
:i1 = newseq_words.index('entity_1_end') - 1#TODO: Use head of entity-phrase instead of rightmost word
why minus just 1? It seems to have conflict with the comment.
The original paper didn't mention the multi-word issue. But there are multi-word entities in the dataset(very few though). I wonder if the proposed model is better at single word entity than multi-word one.
Btw, the evaluation is:
It's not quite good compared the results from paper. You said you used it in SemEval 2018, how was the performance? Can you provide some advice to improve it?
Thanks.