def read_corpus_inputs(args):
"""
Read and filter the part of the corpus we want features for
"""
reader = educe.stac.Reader(args.corpus)
anno_files = reader.filter(reader.files(),
mk_is_interesting(args, args.single))
corpus = reader.slurp(anno_files, verbose=True)
if not args.ignore_cdus:
strip_cdus(corpus, mode=args.strip_mode)
postags = postag.read_tags(corpus, args.corpus)
parses = corenlp.read_results(corpus, args.corpus)
_fuse_corpus(corpus, postags)
for lex in LEXICONS:
lex.read(args.resources)
pdtb_lex = read_pdtb_lexicon(args)
inq_lex = {} # _read_inquirer_lexicon(args)
verbnet_entries = [
VerbNetEntry(x, frozenset(vnet.lemmas(x))) for x in VERBNET_CLASSES
]
return FeatureInput(corpus=corpus,
postags=postags,
parses=parses,
lexicons=LEXICONS,
pdtb_lex=pdtb_lex,
verbnet_entries=verbnet_entries,
inquirer_lex=inq_lex)
def _main_enclosure_graph(args):
"""
Draw graphs showing which annotations' spans include the others
"""
corpus = _read_corpus(args)
output_dir = get_output_dir(args)
keys = corpus
if args.tokens:
postags = postag.read_tags(corpus, args.corpus)
else:
postags = None
for k in sorted(keys):
if postags:
gra_ = stacgraph.EnclosureGraph(corpus[k], postags[k])
else:
gra_ = stacgraph.EnclosureGraph(corpus[k])
if args.reduce:
gra_.reduce()
dot_gra = stacgraph.EnclosureDotGraph(gra_)
if dot_gra.get_nodes():
dot_gra.set("ratio", "compress")
write_dot_graph(k, output_dir, dot_gra,
run_graphviz=args.draw)
else:
print("Skipping %s (empty graph)" % k, file=sys.stderr)
def read_corpus_inputs(args):
"""
Read and filter the part of the corpus we want features for
"""
reader = educe.stac.Reader(args.corpus)
anno_files = reader.filter(reader.files(),
mk_is_interesting(args, args.single))
corpus = reader.slurp(anno_files, verbose=True)
if not args.ignore_cdus:
strip_cdus(corpus)
postags = postag.read_tags(corpus, args.corpus)
parses = corenlp.read_results(corpus, args.corpus)
_fuse_corpus(corpus, postags)
for lex in LEXICONS:
lex.read(args.resources)
pdtb_lex = read_pdtb_lexicon(args)
inq_lex = {} #_read_inquirer_lexicon(args)
verbnet_entries = [VerbNetEntry(x, frozenset(vnet.lemmas(x)))
for x in VERBNET_CLASSES]
return FeatureInput(corpus=corpus,
postags=postags,
parses=parses,
lexicons=LEXICONS,
pdtb_lex=pdtb_lex,
verbnet_entries=verbnet_entries,
inquirer_lex=inq_lex)
def _read_corpus_inputs(args):
"""
Read and filter the part of the corpus we want features for
"""
is_interesting = mk_is_interesting(args, preselected={"stage": ["units"]})
reader = educe.stac.Reader(args.corpus)
anno_files = reader.filter(reader.files(), is_interesting)
corpus = reader.slurp(anno_files, verbose=True)
postags = postag.read_tags(corpus, args.corpus)
parses = corenlp.read_results(corpus, args.corpus)
LEXICON.read(args.resources)
return FeatureInput(corpus=corpus,
postags=postags,
parses=parses,
lexicons=[LEXICON],
pdtb_lex=None,
verbnet_entries=None,
inquirer_lex=None)
def _read_corpus_inputs(args):
"""
Read and filter the part of the corpus we want features for
"""
is_interesting = mk_is_interesting(args,
preselected={"stage": ["units"]})
reader = educe.stac.Reader(args.corpus)
anno_files = reader.filter(reader.files(), is_interesting)
corpus = reader.slurp(anno_files, verbose=True)
postags = postag.read_tags(corpus, args.corpus)
parses = corenlp.read_results(corpus, args.corpus)
LEXICON.read(args.resources)
return FeatureInput(corpus=corpus,
postags=postags,
parses=parses,
lexicons=[LEXICON],
pdtb_lex=None,
verbnet_entries=None,
inquirer_lex=None)
