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Classifying Relations by Ranking with Convolutional Neural Networks
请问代码中的“en”在哪里
nlp = spacy.load('en')
What is the performance that you achieve with the implemented model?
It iteration 100 times ,the best train result in 65 iteration .
I use the SemEval2010_task8_all_data data
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:
precision recall f1-score support
micro avg 0.50 0.50 0.50 2717
macro avg 0.43 0.42 0.38 2717
weighted avg 0.47 0.50 0.45 2717
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.
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "train_crcnn.py", line 93, in main
dftrain = load_sents_data_semeval2010(train_data_file)
File "train_crcnn.py", line 61, in load_sents_data_semeval2010
df = read_semeval2010_data(data_file)
File "/home/CRCNN-master/dataio.py", line 108, in read_semeval2010_data
_, sent = line.split('\t')
ValueError: too many values to unpack (expected 2)
why the output of loss is Nan ?
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