def parse(self, doc):
"""Parse
"""
corenlp_out_name = _guess_corenlp_name(doc.key)
if corenlp_out_name is None:
return doc
fname = os.path.join(self.corenlp_out_dir,
corenlp_out_name)
if not os.path.exists(fname):
raise ValueError('CoreNLP XML: no file {}'.format(fname))
# CoreNLP XML output reader
# FIXME the same reading is done in tokenize(), should find
# a way to cache or share call
reader = PreprocessingSource()
reader.read(fname, suffix='')
corenlp_out = read_corenlp_result(doc, reader)
# ctrees and lexical heads on their nodes
ctrees = corenlp_out.trees
# strip function tags
# TODO maybe this should be an internal preprocessing step in
# find_lexical_heads(), so as to keep the function tags
# that are kept by default by CoreNLP parser because they were found
# to be useful e.g. `-retainTMPSubcategories`
ctrees_no_gf = [transform_tree(ctree, strip_subcategory)
for ctree in ctrees]
lex_heads = [find_lexical_heads(ctree_no_gf)
for ctree_no_gf in ctrees_no_gf]
# store trees in doc
doc.set_syn_ctrees(ctrees_no_gf, lex_heads=lex_heads)
return doc
def tokenize(self, doc):
"""Tokenize the document text.
Parameters
----------
doc: educe.rst_dt.DocumentPlus
Document
Returns
-------
doc: educe.rst_dt.DocumentPlus
Tokenized document
"""
corenlp_out_name = _guess_corenlp_name(doc.key)
if corenlp_out_name is None:
return doc
fname = os.path.join(self.corenlp_out_dir,
corenlp_out_name)
if not os.path.exists(fname):
raise ValueError('CoreNLP XML: no file {}'.format(fname))
# CoreNLP XML output reader
reader = PreprocessingSource()
reader.read(fname, suffix='')
corenlp_out = read_corenlp_result(doc, reader)
# modify DocumentPlus doc to add tokens
doc.set_tokens(corenlp_out.tokens)
return doc
def tokenize(self, doc):
"""Tokenize the document text.
Parameters
----------
doc: educe.rst_dt.DocumentPlus
Document
Returns
-------
doc: educe.rst_dt.DocumentPlus
Tokenized document
"""
corenlp_out_name = _guess_corenlp_name(doc.key)
if corenlp_out_name is None:
return doc
fname = os.path.join(self.corenlp_out_dir, corenlp_out_name)
if not os.path.exists(fname):
raise ValueError('CoreNLP XML: no file {}'.format(fname))
# CoreNLP XML output reader
reader = PreprocessingSource()
reader.read(fname, suffix='')
corenlp_out = read_corenlp_result(doc, reader)
# modify DocumentPlus doc to add tokens
doc.tkd_tokens.extend(corenlp_out.tokens)
return doc
def parse(self, doc):
"""Parse
"""
corenlp_out_name = _guess_corenlp_name(doc.key)
if corenlp_out_name is None:
return doc
fname = os.path.join(self.corenlp_out_dir, corenlp_out_name)
if not os.path.exists(fname):
raise ValueError('CoreNLP XML: no file {}'.format(fname))
# CoreNLP XML output reader
# FIXME the same reading is done in tokenize(), should find
# a way to cache or share call
reader = PreprocessingSource()
reader.read(fname, suffix='')
corenlp_out = read_corenlp_result(doc, reader)
# ctrees and lexical heads on their nodes
ctrees = corenlp_out.trees
# strip function tags
# TODO maybe this should be an internal preprocessing step in
# find_lexical_heads(), so as to keep the function tags
# that are kept by default by CoreNLP parser because they were found
# to be useful e.g. `-retainTMPSubcategories`
ctrees_no_gf = [
transform_tree(ctree, strip_subcategory) for ctree in ctrees
]
lex_heads = [
find_lexical_heads(ctree_no_gf) for ctree_no_gf in ctrees_no_gf
]
# store trees in doc
doc.set_syn_ctrees(ctrees_no_gf, lex_heads=lex_heads)
return doc
def read_results(corpus, dir_name):
"""
Read stored parser output from a directory, and convert them to
educe.annotation.Standoff objects.
Return a dictionary mapping 'FileId's to sets of tokens.
"""
results = {}
for k in corpus:
reader = PreprocessingSource()
reader.read(parsed_file_name(k, dir_name), suffix='')
doc = corpus[k]
results[k] = read_corenlp_result(doc, reader)
return results
def read_results(corpus, dir_name):
"""
Read stored parser output from a directory, and convert them to
educe.annotation.Standoff objects.
Return a dictionary mapping 'FileId's to sets of tokens.
"""
results = {}
for k in corpus:
reader = PreprocessingSource()
reader.read(parsed_file_name(k, dir_name), suffix='')
doc = corpus[k]
results[k] = read_corenlp_result(doc, reader)
return results
encoding='ascii')
# read the RST corpus
rst_reader = Reader(corpus_dir)
rst_corpus = rst_reader.slurp()
# for each file, compare tokenizations between PTB and CoreNLP
for key, rst_tree in sorted(rst_corpus.items()):
doc_name = key.doc.split('.', 1)[0]
if doc_name.startswith('wsj_'):
print(doc_name)
doc_wsj_num = doc_name.split('_')[1]
section = doc_wsj_num[:2]
# corenlp stuff
core_fname = os.path.join(CORENLP_OUT_DIR, corpus,
doc_name + '.out.xml')
core_reader = PreprocessingSource()
core_reader.read(core_fname, suffix='')
corenlp_doc = read_corenlp_result(None, core_reader)
core_toks = corenlp_doc.tokens
core_toks_beg = [x.span.char_start for x in core_toks]
core_toks_end = [x.span.char_end for x in core_toks]
# PTB stuff
# * create DocumentPlus (adapted from educe.rst_dt.corpus)
rst_context = rst_tree.label().context
ptb_docp = DocumentPlus(key, doc_name, rst_context)
# * attach EDUs (yerk)
# FIXME we currently get them via an RstDepTree created from
# the original RSTTree, so as to get the left padding EDU
rst_dtree = RstDepTree.from_rst_tree(rst_tree)
ptb_docp.edus = rst_dtree.edus
encoding='ascii')
# read the RST corpus
rst_reader = Reader(corpus_dir)
rst_corpus = rst_reader.slurp()
# for each file, compare tokenizations between PTB and CoreNLP
for key, rst_tree in sorted(rst_corpus.items()):
doc_name = key.doc.split('.', 1)[0]
if doc_name.startswith('wsj_'):
print(doc_name)
doc_wsj_num = doc_name.split('_')[1]
section = doc_wsj_num[:2]
# corenlp stuff
core_fname = os.path.join(CORENLP_OUT_DIR, corpus,
doc_name + '.out.xml')
core_reader = PreprocessingSource()
core_reader.read(core_fname, suffix='')
corenlp_doc = read_corenlp_result(None, core_reader)
core_toks = corenlp_doc.tokens
core_toks_beg = [x.span.char_start for x in core_toks]
core_toks_end = [x.span.char_end for x in core_toks]
# PTB stuff
# * create DocumentPlus (adapted from educe.rst_dt.corpus)
rst_context = rst_tree.label().context
ptb_docp = DocumentPlus(key, doc_name, rst_context)
# * attach EDUs (yerk)
# FIXME we currently get them via an RstDepTree created from
# the original RSTTree, so as to get the left padding EDU
rst_dtree = RstDepTree.from_rst_tree(rst_tree)
ptb_docp.edus = rst_dtree.edus
