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Encountering errors in running predict_network.py, e.g.,
File "predict_network.py", line 327, in <module>
main()
File "predict_network.py", line 121, in main
tmp_note = TimeNote(tml, tml)
File "/homes/hny2/mfeb/causal/event-based-reasoning/code/notes/TimeNote.py", line 46, in __init__
tokenized_text, token_to_offset, sentence_features, dependency_paths, id_to_tok = pre_processing.pre_process(self.original_text)
File "/homes/hny2/mfeb/causal/event-based-reasoning/code/notes/utilities/pre_processing/pre_processing.py", line 26, in pre_process
naf_tagged_doc = pre_processor.pre_process(text) # output a string of xml doc
File "/homes/hny2/mfeb/causal/event-based-reasoning/code/notes/utilities/pre_processing/news_reader.py", line 68, in pre_process
srl_text = srl.parse_dependencies(constituency_parsed_text)
File "/homes/hny2/mfeb/causal/event-based-reasoning/code/notes/utilities/pre_processing/news_reader.py", line 124, in parse_dependencies
return SRLClient.parse_dependencies(naf_tokenized_pos_tagged_text)
File "/homes/hny2/mfeb/causal/event-based-reasoning/code/notes/utilities/pre_processing/news_reader.py", line 159, in parse_dependencies
_output, _ = srl.communicate(naf_tokenized_pos_tagged_text)
File "/homes/hny2/mfeb/anaconda3/envs/py27/lib/python2.7/subprocess.py", line 479, in communicate
return self._communicate(input)
File "/homes/hny2/mfeb/anaconda3/envs/py27/lib/python2.7/subprocess.py", line 1102, in _communicate
stdout, stderr = self._communicate_with_poll(input)
File "/homes/hny2/mfeb/anaconda3/envs/py27/lib/python2.7/subprocess.py", line 1166, in _communicate_with_poll
input_offset += os.write(fd, chunk)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 1787: ordinal not in range(128)
This is just one of many such errors.
Web postings indicate that the use of str() is the culprit in these cases, recommending the use of, e.g., .encode('utf-8'), instead. It's not clear, though, where the root cause might be. The trace indicates that it's writing the results of the parsing subprocess.
Any recommendations for addressing this?
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