Write your own custom split-function: input_string = “””{J. Doe, R. Starr}, {Lorem {i}psum dolor }, Dol. sit., am. et.””” expected = [‘{J. Doe, R. Starr}’, ‘{Lorem\n{i}psum dolor }’, ‘Dol. sit.’, ‘am. et.’] def split(s): parts = [] bracket_level = 0 current = [] # trick to remove special-case of trailing chars for c in (s … Read more
I have looked into this issue quite extensively. Python-sqlparse is a non validating parser which is not really what you need. The examples in antlr need a lot of work to convert to a nice ast in python. The sql standard grammars are here, but it would be a full time job to convert them … Read more
I received the same error and I’m also in Python 3.6.0 … AttributeError: ‘version_info’ object has no attribute ‘__version__’ If you want to dig a little more, you can type this in your console and detect which package is using this dependency. > pip show pyparsing In my case the output was something like this, … Read more
As Harrison pointed out, nltk has named entity recognition, which is what you want for this task. Here is a good sample to get you started. From the site: import nltk sentences = nltk.sent_tokenize(text) tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences] tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True) def extract_entity_names(t): entity_names … Read more