Comments (21)
Unfortunately, I have never tested it in a Windows environment. I imagine firing up the HTTP server includes POSIX threads.
To my knowledge this is only available in Unix environments.
It should work on any MacOS/Linux/Unix environment though...
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It appears you have not installed Java? or maybe it's not in your PATH?
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from negation-detection.
Try reducing the heap size, i.e.
-Xmx2g
maybe?
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-
for the last error you probably need to install setuptools (google it, depends on your current python installation)
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for the negation error, it appears you are either trying to run it from a different folder (stanford core nlp) or that you have not installed stanford_corenlp_pywrapper (see installation guideline in my repo)
Else, try removing everything and starting from scratch; if you follow the steps one by one, I believe you may be successful
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hmm, it appears this may be an error.
try negation_detection.predict(sentence, 'suicide')
let me know if this worked
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try:
import nltk
nltk.download('all')
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That is great!
Assuming you used the example sentence, this is indeed correct (i.e. 'suicide attempts' is affirmed/True).
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Hi,
I am afraid I cannot share/publish this dataset. The dataset originates from real-world Electronic Health Records and I am not allowed to share this data.
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@thavasimaniraj @gkotsis How did you get the parse key value when you have used mode as 'pos'.
have you used shift-reduce parse?
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Python 3.6.10 |Anaconda, Inc.| (default, Jan 7 2020, 15:01:53)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
import negation_detection
INFO:CoreNLP_PyWrapper:mode given as 'parse' so setting annotators: tokenize, ssplit, pos, lemma, parse
INFO:CoreNLP_PyWrapper:Starting java subprocess, and waiting for signal it's ready, with command: exec java -Xmx4g -XX:ParallelGCThreads=1 -cp '/Users/kaushikjaiswal/anaconda3/envs/DOS/lib/python3.6/site-packages/stanford_corenlp_pywrapper/lib/:/Users/kaushikjaiswal/Desktop/Negation/negation-detection/stanford-corenlp-full-2018-10-05/' corenlp.SocketServer --outpipe /tmp/corenlp_pywrap_pipe_pypid=6089_time=1586333049.602601 --configdict '{"annotators":"tokenize, ssplit, pos, lemma, parse"}'
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator tokenize
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator ssplit
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator pos
[main] INFO edu.stanford.nlp.tagger.maxent.MaxentTagger - Loading POS tagger from edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger ... done [1.4 sec].
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator lemma
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator parse
[main] INFO edu.stanford.nlp.parser.common.ParserGrammar - Loading parser from serialized file edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz ... done [1.0 sec].
INFO:CoreNLP_JavaServer: CoreNLP pipeline initialized.
INFO:CoreNLP_JavaServer: Waiting for commands on stdin
INFO:CoreNLP_PyWrapper:Successful ping. The server has started.
INFO:CoreNLP_PyWrapper:Subprocess is ready.
Traceback (most recent call last):
File "", line 1, in
File "/Users/kaushikjaiswal/Desktop/Negation/negation-detection/negation_detection.py", line 439, in predict
INFO:CoreNLP_JavaServer: INPUT: 1 documents, 101 characters, 19 tokens, 101.0 char/doc, 19.0 tok/doc RATES: 0.880 doc/sec, 16.7 tok/sec
sentence = preprocess(sentence, keyword)
File "/Users/kaushikjaiswal/Desktop/Negation/negation-detection/negation_detection.py", line 152, in preprocess
for e in rs['sentences']:
TypeError: 'NoneType' object is not subscriptable
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{"sentences":
[
{ "pos":["NNP","VBD","DT","JJ","NNS","IN","JJ","NN",",","VBD","WRB","IN","DT","JJ","PRP","VBD","JJ","NN","NNS"],
"lemmas":["ZZZZ","report","no","recent","period","of","low","mood",",","discuss","how","in","the","past","she","make","many","suicide","attempt"],
"tokens":["ZZZZ","reported","no","recent","periods","of","low","mood",",","discussed","how","in","the","past","she","made","many","suicide","attempts"],
"char_offsets":[[0,4],[5,13],[14,16],[17,23],[24,31],[32,34],[35,38],[39,43],[43,44],[45,54],[55,58],[59,61],[62,65],[66,70],[71,74],[75,79],[80,84],[85,92],[93,101]]
}
]
}
There is no parse key in it then how did you get the values when the p = tmp['sentences'][i]['parse'] of the below function gets called:
def findSentencePTreeToken(sentence, keyword):
import nltk
from nltk.tree import ParentedTree
stemmed = _lemma_(keyword)
tmp = proc.parse_doc(sentence)
i = 0
numSentences = len(tmp['sentences'])
rs = []
for i in range(0, numSentences):
p = tmp['sentences'][i]['parse']
ptree = ParentedTree.fromstring(p)
# rs = []
for i in range(0, len(ptree.leaves())):
tree_position = ptree.leaf_treeposition(i)
node = ptree[tree_position]
if _stem_(node)==stemmed:
tree_position = tree_position[0:len(tree_position)-1]
rs.append(ptree[tree_position])
# if len(rs)>0:
# return rs
return rs
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