def parse(text_file):
with open(text_file, "rb") as f:
text = unicode(f.read())
if text_file.endswith(".conll10"):
edited = text
elif text_file.endswith(".txt"):
# Get parse from Spacy
from spacy.en import English
parser = English(entity=False,
load_vectors=False,
vectors_package=False)
parsed = parser(text)
# Transform to conll format
parsed_string = ""
prev_s_toks = 0
for sent in parsed.sents:
toks = 0
for index, token in enumerate(sent):
toks += 1
# skip if token is newline.
if token.orth_ == "\n":
continue
if token.head.i + 1 - prev_s_toks == token.i + 1 - prev_s_toks:
head_id = 0
else:
head_id = token.head.i + 1 - prev_s_toks
line = [
str(token.i + 1 - prev_s_toks), token.orth_, token.lemma_,
token.tag_, token.pos_, "_",
str(head_id), token.dep_, "_", "_"
]
parsed_string += "\t".join(line) + "\n"
prev_s_toks += toks
parsed_string += "\n"
# setup config
config = "func=/ROOT/\tnone\t#1:func=root\n"
config += "func=/relcl/\tnone\t#1:func=rcmod\n"
config += "func=/nummod/\tnone\t#1:func=num\n"
config += "lemma=/be/&func=/(.*)/;func=/nsubj/;func=/attr/;text=/.*/\t#1>#2;#1>#3;#4>#1\t#4>#3;#3>#1;#3>#2;#1:func=cop;#3:func=$1\n"
config += "lemma=/be/&func=/root/;func=/nsubj/;func=/attr/\t#1>#2;#1>#3\t#3>#1;#3>#2;#1:func=cop;#3:func=root\n"
# Setup depedit
deped = DepEdit(config.split("\n"))
# Run depedit
edited = deped.run_depedit(parsed_string.split("\n"))
else:
sys.exit("Unknown input file type")
# Get and return Xrenner object
xobj = Xrenner(override="GUM")
xobj.analyze(edited, "conll")
return xobj
def __init__(self, model="eng", override=None):
"""
Main class for xrenner coreferencer. Invokes the load method to read model data.
:param model: model directory in models/ specifying settings and gazetteers for this language (default: eng)
:param override: name of a section in models/override.ini if configuration overrides should be applied
:return: void
"""
self.load(model, override)
depedit_config = self.lex.model_files["depedit.ini"]
self.depedit = DepEdit(depedit_config)
def add_space_after(plain_text, conllu):
"""Adds the SpaceAfter=No feature to conllu data based on original plain text, ignoring XML"""
def chomp(plain_text, token):
space_prop = ""
token_index = plain_text.find(token)
plain_text = plain_text[token_index + len(token):]
in_xml = False
for c in plain_text:
if c in [" ", "\n", "\t", "\r"]:
space_prop = ""
break
elif c == "<":
in_xml = True
elif c == ">":
in_xml = False
elif not in_xml:
# Non-whitespace character outside of XML, there was no space after the token
space_prop = "SpaceAfter=No"
break
return space_prop, plain_text
plain_text = re.sub(r'<[^<>]+>', "", plain_text) # Destroy XML
output = []
skip = 0
lines = conllu.split("\n")
for line in lines:
if "\t" in line:
fields = line.split("\t")
if "-" in fields[0]: # Supertoken
start, end = fields[0].split("-")
skip = int(end) - int(start) + 1
space_prop, plain_text = chomp(plain_text, fields[1])
if space_prop != "":
fields[-1] = add_feat(fields[-1], space_prop)
line = "\t".join(fields)
else:
if skip > 0:
# This is inside a supertoken, no need to add SpaceAfter, just down-tick skip
skip -= 1
else:
space_prop, plain_text = chomp(plain_text, fields[1])
if space_prop != "":
fields[-1] = add_feat(fields[-1], space_prop)
line = "\t".join(fields)
output.append(line)
output = "\n".join(output)
#output = move_binyan(output)
d = DepEdit(config_file=[])
output = d.run_depedit(output, sent_text=True)
return output
if text in entdict.keys():
# Check if new ent is diff, if so add
if type not in entdict[text]:
entdict[text] += [type]
else:
entdict[text] = [type]
seen_unknown = set([])
seen_ambig = set([])
freqs_unknown = defaultdict(int)
entity_deps = defaultdict(int)
if options.config_depedit != "":
from depedit import DepEdit
d = DepEdit(config_file=options.config_depedit)
else:
d = None
for filename in glob.glob(options.dep_files): # Loops over conll file(s)
with io.open(filename, encoding="utf8") as f:
myfile = f.read()
if d is not None:
myfile = d.run_depedit(myfile.split("\n"))
conll_tokens = []
for myline in myfile.split("\n"):
if myline.find(
from nlp_modules.base import NLPModule, PipelineDep, NLPDependencyException
import torch, flair
from depedit import DepEdit
from flair.data import Sentence
from flair.models import SequenceTagger
from udapi.core.document import Document as UdapiDocument
from udapi.block.ud.fixpunct import FixPunct
#flair.device = torch.device('cpu') # Uncomment to use CPU for flair
script_dir = os.path.dirname(os.path.realpath(__file__)) + os.sep
nlp_modules_dir = script_dir + ".." + os.sep + "nlp_modules" + os.sep
parser_dep_dir = nlp_modules_dir + "parser-dependencies" + os.sep
gum_root = "GUM" + os.sep
depedit1 = DepEdit(config_file=parser_dep_dir + "postprocess_parser.ini")
depedit2 = DepEdit(config_file=parser_dep_dir + "upos.ini")
depedit3 = DepEdit(config_file=parser_dep_dir +
"eng_morph_enhance_no_stype.ini")
vocab = set(
io.open(parser_dep_dir + "eng_vocab.tab",
encoding="utf8").read().strip().split("\n"))
def fix_punct(conllu_string):
conllu_string = re.sub(r"\t'\t([^\t\n]+\tPART\tPOS)",
r'\t&udapi_apos;\t\1',
conllu_string,
flags=re.MULTILINE)
conllu_string = re.sub(
if PY3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
inp = input if PY3 else raw_input
script_dir = os.path.dirname(os.path.realpath(__file__))
lib_dir = script_dir + os.sep + "lib" + os.sep
bin_dir = script_dir + os.sep + "bin" + os.sep
data_dir = script_dir + os.sep + "data" + os.sep
model_dir = script_dir + os.sep + "models" + os.sep
marmot_path = bin_dir + "Marmot" + os.sep
KNOWN_PUNCT = {'’','“','”'} # Hardwired tokens to tag as punctutation (unicode glyphs not in training data)
morph_deped = DepEdit(config_file=lib_dir+"partial_morph.ini")
tags = {"NOUN", "VERB", "ADJ", "ADV", "PROPN"}
lex_data = open(data_dir + "heb.lemma", encoding="utf8").readlines()
lex = {}
for l in lex_data:
word, tag, lemma = l[:-1].split("\t")
if tag in tags:
lex["\t".join([word,tag])] = lemma
binyan_data = open(data_dir + "heb.binyan", encoding="utf8").readlines()
binyan_lookup = {}
binyan_lemma_lookup = {}
for l in binyan_data:
word, lemma, binyan = l[:-1].split("\t")
binyan_lookup[(word,lemma)] = binyan
binyan_lemma_lookup[lemma] = binyan
class Xrenner:
def __init__(self, model="eng", override=None):
"""
Main class for xrenner coreferencer. Invokes the load method to read model data.
:param model: model directory in models/ specifying settings and gazetteers for this language (default: eng)
:param override: name of a section in models/override.ini if configuration overrides should be applied
:return: void
"""
self.load(model, override)
depedit_config = self.lex.model_files["depedit.ini"]
self.depedit = DepEdit(depedit_config)
def load(self, model="eng", override=None):
"""
Method to load model data. Normally invoked by constructor, but can be repeated to change models later.
:param model: model directory in models/ specifying settings and gazetteers for this language (default: eng)
:param override: name of a section in models/override.ini if configuration overrides should be applied
:return: void
"""
self.model = model
self.override = override
self.lex = LexData(self.model, self.override)
def set_doc_name(self, name):
"""
Method to manually set the name of the document being processed, rather than deriving it from an input file name.
:param name: string, the name to give the document
:return: void
"""
self.docname = name
def analyze(self, infile, out_format):
"""
Method to run coreference analysis with loaded model
:param infile: file name of the parse file in the conll10 format, or the pre-read parse itself
:param format: format to determine output type, one of: html, paula, webanno, conll, onto, unittest
:return: output based on requested format
"""
# Check if this is a file name from the main script or a parse delivered in an import or unittest scenario
if "\t" in infile or isinstance(
infile, list
): # This is a raw parse as string or list, not a file name
self.docpath = os.path.dirname(os.path.abspath("."))
self.docname = "untitled"
if not isinstance(infile, list):
infile = infile.replace("\r", "").split("\n")
else: # This is a file name, extract document name and path, then read the file
self.docpath = os.path.dirname(os.path.abspath(infile))
self.docname = clean_filename(ntpath.basename(infile))
infile = open(infile)
# Empty cached lists of incompatible pairs
self.lex.incompatible_mod_pairs = set([])
self.lex.incompatible_isa_pairs = set([])
infile = self.depedit.run_depedit(infile)
infile = infile.split("\n")
# Lists and dictionaries to hold tokens and markables
self.conll_tokens = []
self.conll_tokens.append(
ParsedToken(0, "ROOT", "--", "XX", "", -1, "NONE",
Sentence(1, 0, ""), [], [], [], self.lex))
self.markables = []
self.markables_by_head = OrderedDict()
self.markstart_dict = defaultdict(list)
self.markend_dict = defaultdict(list)
self.tokoffset = 0
self.sentlength = 0
self.markcounter = 1
self.groupcounter = 1
self.children = defaultdict(list)
self.descendants = {}
self.child_funcs = defaultdict(list)
self.child_strings = defaultdict(list)
# Dereference object classes to method globals for convenience
lex = self.lex
conll_tokens = self.conll_tokens
markstart_dict = self.markstart_dict
markend_dict = self.markend_dict
self.sent_num = 1
quoted = False
current_sentence = Sentence(self.sent_num, self.tokoffset, "")
lex.coref_rules = lex.non_speaker_rules
for myline in infile:
if "#speaker" in myline: # speaker
current_sentence.speaker = myline.split('"')[1]
lex.coref_rules = lex.speaker_rules
elif myline.find(
"\t"
) > 0: # Only process lines that contain tabs (i.e. conll tokens)
current_sentence.token_count += 1
cols = myline.split("\t")
if lex.filters["open_quote"].match(
cols[1]) is not None and quoted is False:
quoted = True
elif lex.filters["close_quote"].match(
cols[1]) is not None and quoted is True:
quoted = False
if lex.filters["question_mark"].match(cols[1]) is not None:
current_sentence.mood = "question"
if cols[3] in lex.func_substitutes_forward and int(
cols[6]) > int(cols[0]):
tok_func = re.sub(lex.func_substitutes_forward[cols[3]][0],
lex.func_substitutes_forward[cols[3]][1],
cols[7])
elif cols[3] in lex.func_substitutes_backward and int(
cols[6]) < int(cols[0]):
tok_func = re.sub(
lex.func_substitutes_backward[cols[3]][0],
lex.func_substitutes_backward[cols[3]][1], cols[7])
else:
tok_func = cols[7]
head_id = "0" if cols[6] == "0" else str(
int(cols[6]) + self.tokoffset)
conll_tokens.append(
ParsedToken(str(int(cols[0]) + self.tokoffset), cols[1],
cols[2], cols[3], cols[5], head_id, tok_func,
current_sentence, [], [], [], lex, quoted,
cols[8], cols[9]))
self.sentlength += 1
# Check not to add a child if this is a function which discontinues the markable span
if not (lex.filters["non_link_func"].match(tok_func)
is not None or lex.filters["non_link_tok"].match(
cols[1]) is not None):
if cols[6] != "0": # Do not add children to the 'zero' token
self.children[str(
int(cols[6]) + self.tokoffset)].append(
str(int(cols[0]) + self.tokoffset))
self.child_funcs[(int(cols[6]) +
self.tokoffset)].append(tok_func)
self.child_strings[(int(cols[6]) + self.tokoffset)].append(
cols[1])
elif self.sentlength > 0:
self.process_sentence(self.tokoffset, current_sentence)
self.sent_num += 1
if self.sentlength > 0:
self.tokoffset += self.sentlength
current_sentence = Sentence(self.sent_num, self.tokoffset, "")
self.sentlength = 0
# Handle leftover sentence which did not have trailing newline
if self.sentlength > 0:
self.process_sentence(self.tokoffset, current_sentence)
marks_to_add = []
if lex.filters["seek_verb_for_defs"]:
for mark in self.markables:
if mark.definiteness == "def" and mark.antecedent == "none" and mark.form == "common" and \
(lex.filters["event_def_entity"] == mark.entity or lex.filters["abstract_def_entity"] == mark.entity):
for tok in conll_tokens[0:mark.start]:
if lex.filters["verb_head_pos"].match(tok.pos):
if stems_compatible(tok, mark.head, lex):
v_antecedent = make_markable(
tok, conll_tokens, {},
tok.sentence.start_offset, tok.sentence,
[], lex)
mark.antecedent = v_antecedent
mark.coref_type = "coref"
v_antecedent.entity = mark.entity
v_antecedent.subclass = mark.subclass
v_antecedent.definiteness = "none"
v_antecedent.form = "verbal"
v_antecedent.infstat = "new"
v_antecedent.group = mark.group
v_antecedent.id = "referent_" + v_antecedent.head.id
marks_to_add.append(v_antecedent)
for mark in marks_to_add:
markstart_dict[mark.start].append(mark)
markend_dict[mark.end].append(mark)
self.markables_by_head[mark.head.id] = mark
self.markables.append(mark)
postprocess_coref(self.markables, lex, markstart_dict, markend_dict,
self.markables_by_head, conll_tokens)
if out_format == "paula":
try:
self.serialize_output(out_format)
return True
except:
return False
else:
return self.serialize_output(out_format, infile)
def analyze_markable(self, mark, lex):
"""
Find entity, agreement and cardinality information for a markable
:param mark: The :class:`.Markable` object to analyze
:param lex: the :class:`.LexData` object with gazetteer information and model settings
:return: void
"""
mark.text = mark.text.strip()
mark.core_text = mark.core_text.strip()
# DEBUG POINT
if mark.text == lex.debug["ana"]:
pass
tok = mark.head
if lex.filters["proper_pos"].match(tok.pos) is not None:
mark.form = "proper"
mark.definiteness = "def"
elif lex.filters["pronoun_pos"].match(tok.pos) is not None:
mark.form = "pronoun"
# Check for explicit indefinite morphology in morph feature of head token
if "indef" in mark.head.morph.lower():
mark.definiteness = "indef"
else:
mark.definiteness = "def"
else:
mark.form = "common"
# Check for explicit definite morphology in morph feature of head token
if "def" in mark.head.morph.lower(
) and "indef" not in mark.head.morph.lower():
mark.definiteness = "def"
# Chomp definite information not to interfere with agreement
mark.head.morph = re.sub("def", "_", mark.head.morph)
else:
# Check if any children linked via a link function are definite markings
children_are_def_articles = (
lex.filters["definite_articles"].match(maybe_article)
is not None for maybe_article in
[mark.head.text, mark.text.split(" ")[0]] +
mark.head.child_strings)
if any(children_are_def_articles):
mark.definiteness = "def"
else:
mark.definiteness = "indef"
# Find agreement alternatives unless cardinality has set agreement explicitly already (e.g. to 'plural'/'dual' etc.)
if mark.cardinality == 0 or mark.agree == '':
mark.alt_agree = resolve_mark_agree(mark, lex)
if mark.alt_agree is not None and mark.agree == '':
mark.agree = mark.alt_agree[0]
elif mark.alt_agree is None:
mark.alt_agree = []
if mark.agree != mark.head.morph and mark.head.morph != "_" and mark.head.morph != "--" and mark.agree != \
lex.filters["aggregate_agree"]:
mark.agree = mark.head.morph
mark.agree_certainty = "mark_head_morph"
mark.alt_agree.append(mark.head.morph)
# cardinality resolve, only resolve here if it hasn't been set before (as in coordination markable)
if mark.cardinality == 0:
mark.cardinality = resolve_cardinality(mark, lex)
resolve_mark_entity(mark, lex)
if "ablations" in lex.debug:
if "no_subclasses" in lex.debug["ablations"]:
mark.subclass = mark.entity
mark.alt_subclasses = mark.alt_entities
def serialize_output(self, out_format, parse=None):
"""
Return a string representation of the output in some format, or generate PAULA directory structure as output
:param out_format: the format to generate, one of: html, paula, webanno, conll, onto, unittest
:param parse: the original parse input fed to xrenner; only needed for unittest output
:return: specified output format string, or void for paula
"""
conll_tokens = self.conll_tokens
markables, markstart_dict, markend_dict = self.markables, self.markstart_dict, self.markend_dict
if out_format == "html":
rtl = True if self.model in ["heb", "ara"] else False
return output_HTML(conll_tokens, markstart_dict, markend_dict, rtl)
elif out_format == "paula":
output_PAULA(conll_tokens, markstart_dict, markend_dict,
self.docname, self.docpath)
elif out_format == "webanno":
return output_webanno(conll_tokens[1:], markables)
elif out_format == "conll":
return output_conll(conll_tokens, markstart_dict, markend_dict,
self.docname, True)
elif out_format == "onto":
return output_onto(conll_tokens, markstart_dict, markend_dict,
self.docname)
elif out_format == "unittest":
from xrenner_test import generate_test
return generate_test(conll_tokens, markables, parse, self.model)
elif out_format == "none":
return ""
else:
return output_SGML(conll_tokens, markstart_dict, markend_dict)
def process_sentence(self, tokoffset, sentence):
"""
Function to analyze a single sentence
:param tokoffset: the offset in tokens for the beginning of the current sentence within all input tokens
:param sentence: the Sentence object containin mood, speaker and other information about this sentence
:return: void
"""
markables = self.markables
markables_by_head = self.markables_by_head
lex = self.lex
conll_tokens = self.conll_tokens
child_funcs = self.child_funcs
child_strings = self.child_strings
children = self.children
descendants = self.descendants
markstart_dict = self.markstart_dict
markend_dict = self.markend_dict
# Add list of all dependent funcs and strings to each token
add_child_info(conll_tokens, child_funcs, child_strings)
mark_candidates_by_head = OrderedDict()
stop_ids = {}
for tok1 in conll_tokens[tokoffset + 1:]:
stop_ids[tok1.id] = False # Assume all tokens are head candidates
tok1.sent_position = float(
int(tok1.id) - tokoffset
) / sentence.token_count # Add relative token positions at sentence as percentages
tok1.head_text = conll_tokens[int(
tok1.head
)].text # Save parent text for later dependency checks
# Post-process parser input based on entity list if desired
if lex.filters["postprocess_parser"]:
postprocess_parser(conll_tokens, tokoffset, children, stop_ids,
lex)
# Revert conj token function to parent function
replace_conj_func(conll_tokens, tokoffset, lex)
# Enrich tokens with modifiers and parent head text
for token in conll_tokens[tokoffset:]:
for child in children[token.id]:
if lex.filters["mod_func"].match(
conll_tokens[int(child)].func) is not None:
token.modifiers.append(conll_tokens[int(child)])
token.head_text = conll_tokens[int(token.head)].text
# Check for lexical possessives to dynamically enhance hasa information
if lex.filters["possessive_func"].match(token.func) is not None:
# Check that neither possessor nor possessed is a pronoun
if lex.filters["pronoun_pos"].match(
token.pos
) is None and lex.filters["pronoun_pos"].match(
conll_tokens[int(token.head)].pos) is None:
lex.hasa[token.text][conll_tokens[int(
token.head
)].text] += 2 # Increase by 2: 1 for attestation, 1 for pertinence in this document
# Check if func2 has additional possessor information
if token.func2 != "_":
if lex.filters["possessive_func"].match(
token.func2) is not None:
if lex.filters["pronoun_pos"].match(
token.pos
) is None and lex.filters["pronoun_pos"].match(
conll_tokens[int(token.head2) +
tokoffset].pos) is None:
lex.hasa[token.text][
conll_tokens[int(token.head2) + tokoffset].
text] += 2 # Increase by 2: 1 for attestation, 1 for pertinence in this document
# Find dead areas
for tok1 in conll_tokens[tokoffset + 1:]:
# Affix tokens can't be markable heads - assume parser error and fix if desired
# DEBUG POINT
if tok1.text.strip() == lex.debug["ana"]:
pass
if lex.filters["postprocess_parser"]:
if ((lex.filters["mark_head_pos"].match(tok1.pos) is not None
and lex.filters["mark_forbidden_func"].match(tok1.func) is
None) or pos_func_combo(tok1.pos, tok1.func,
lex.filters["pos_func_heads"])
) and not (stop_ids[tok1.id]):
if tok1.text.strip() in lex.affix_tokens:
stop_ids[tok1.id] = True
for child_id in sorted(children[tok1.id],
reverse=True):
child = conll_tokens[int(child_id)]
if ((lex.filters["mark_head_pos"].match(
child.pos) is not None
and lex.filters["mark_forbidden_func"].match(
child.func) is None) or pos_func_combo(
child.pos, child.func,
lex.filters["pos_func_heads"])
) and not (stop_ids[child.id]):
child.head = tok1.head
tok1.head = child.id
# Make the new head be the head of all children of the affix token
for child_id2 in children[tok1.id]:
if not child_id2 == child_id:
conll_tokens[int(
child_id2)].head = child.id
children[tok1.id].remove(child_id2)
children[child.id].append(child_id2)
# Assign the function of the affix head to the new head and vice versa
temp_func = child.func
child.func = tok1.func
tok1.func = temp_func
children[tok1.id].remove(child.id)
children[child.id].append(tok1.id)
if child in tok1.modifiers:
tok1.modifiers.remove(child)
child.modifiers.append(tok1)
# Check if any other non-link parents need to be re-routed to the new head
for tok_to_rewire in conll_tokens[tokoffset +
1:]:
if tok_to_rewire.original_head == tok1.id and tok_to_rewire.head != child.id and tok_to_rewire.id != child.id:
tok_to_rewire.head = child.id
# Also add the rewired child func
if tok_to_rewire.func not in child.child_funcs:
child.child_funcs.append(
tok_to_rewire.func)
# Rewire modifiers
if tok_to_rewire not in child.modifiers and lex.filters[
"mod_func"].match(
tok_to_rewire.func
) is not None:
child.modifiers.append(
tok_to_rewire)
if child in tok_to_rewire.modifiers:
tok_to_rewire.modifiers.remove(
child)
# Only do this for the first subordinate markable head found by traversing right to left
break
# Try to construct a longer stop candidate starting with each token in the sentence, max length 5 tokens
stop_candidate = ""
for tok2 in conll_tokens[int(tok1.id):min(len(conll_tokens),
int(tok1.id) + 4)]:
stop_candidate += tok2.text + " "
if stop_candidate.strip().lower(
) in lex.stop_list: # Stop list matched, flag tokens as impossible markable heads
for tok3 in conll_tokens[int(tok1.id):int(tok2.id) + 1]:
stop_ids[tok3.id] = True
# Find last-first name combinations
for tok1 in conll_tokens[tokoffset + 1:-1]:
tok2 = conll_tokens[int(tok1.id) + 1]
first_name_candidate = tok1.text.title() if tok1.text.isupper(
) else tok1.text
last_name_candidate = tok2.text.title() if tok2.text.isupper(
) else tok2.text
if first_name_candidate in lex.first_names and last_name_candidate in lex.last_names and tok1.head == tok2.id:
stop_ids[tok1.id] = True
# Allow one intervening token, e.g. for middle initial
for tok1 in conll_tokens[tokoffset + 1:-2]:
tok2 = conll_tokens[int(tok1.id) + 2]
first_name_candidate = tok1.text.title() if tok1.text.isupper(
) else tok1.text
middle_name_candidate = conll_tokens[int(tok1.id) + 1].text.title(
) if tok1.text.isupper() else conll_tokens[int(tok1.id) + 1].text
last_name_candidate = tok2.text.title() if tok2.text.isupper(
) else tok2.text
if first_name_candidate in lex.first_names and last_name_candidate in lex.last_names and tok1.head == tok2.id and (
re.match(r'^[A-Z]\.$', middle_name_candidate)
or middle_name_candidate in lex.first_names):
stop_ids[tok1.id] = True
# Expand children list recursively into descendants
for parent_key in children:
if int(parent_key) > tokoffset:
descendants[parent_key] = get_descendants(
parent_key, children, [], self.sent_num, conll_tokens)
keys_to_pop = []
# Find markables
for tok in conll_tokens[tokoffset + 1:]:
# Markable heads should match specified pos or pos+func combinations,
# ruling out stop list items with appropriate functions
if tok.text.strip() == lex.debug["ana"]:
pass
# TODO: consider switch for lex.filters["stop_func"].match(tok.func)
if ((lex.filters["mark_head_pos"].match(tok.pos) is not None and
lex.filters["mark_forbidden_func"].match(tok.func) is None)
or pos_func_combo(tok.pos, tok.func,
lex.filters["pos_func_heads"])
) and not (stop_ids[tok.id]):
this_markable = make_markable(tok, conll_tokens, descendants,
tokoffset, sentence, keys_to_pop,
lex)
if this_markable is not None:
mark_candidates_by_head[tok.id] = this_markable
# Check whether this head is the beginning of a coordination and needs its own sub-markable too
make_submark = False
submark_id = ""
submarks = []
cardi = 0
for child_id in children[tok.id]:
child = conll_tokens[int(child_id)]
if child.coordinate:
# Coordination found - make a small markable for just this first head without coordinates
make_submark = True
# Remove the coordinate children from descendants of small markable head
if child.id in descendants:
for sub_descendant in descendants[child.id]:
if tok.id in descendants:
if sub_descendant in descendants[tok.id]:
descendants[tok.id].remove(
sub_descendant)
if tok.id in descendants:
if child.id in descendants[tok.id]:
descendants[tok.id].remove(child.id)
# Build a composite id for the large head from coordinate children IDs separated by underscore
submark_id += "_" + child.id
cardi += 1
submarks.append(child.id)
if make_submark:
submarks.append(tok.id)
# Assign aggregate/coordinate agreement class to large markable if desired
# Remove coordination tokens, such as 'and', 'or' based on coord_func setting
for child_id in children[tok.id]:
child = conll_tokens[int(child_id)]
if lex.filters["coord_func"].match(child.func):
if child.id in descendants[tok.id]:
descendants[tok.id].remove(child.id)
# Make the small markable and recall the big markable
mark_candidates_by_head[tok.id].cardinality = cardi + 1
big_markable = mark_candidates_by_head[tok.id]
small_markable = make_markable(tok, conll_tokens,
descendants, tokoffset,
sentence, keys_to_pop, lex)
big_markable.submarks = submarks[:]
if lex.filters["aggregate_agree"] != "_":
big_markable.agree = lex.filters["aggregate_agree"]
big_markable.agree_certainty = "coordinate_aggregate_plural"
big_markable.coordinate = True
# Switch the id's so that the big markable has the 1_2_3 style id, and the small has just the head id
mark_candidates_by_head[tok.id + submark_id] = big_markable
mark_candidates_by_head[tok.id] = small_markable
big_markable = None
small_markable = None
# Check for atomicity and remove any subsumed markables if atomic
for mark_id in mark_candidates_by_head:
mark = mark_candidates_by_head[mark_id]
if mark.end > mark.start: # No atomicity check if single token
# Check if the markable has a modifier based entity guess
modifier_based_entity = recognize_entity_by_mod(
mark, lex, True)
# Consider for atomicity if in atoms or has @ modifier, but not if it's a single token or a coordinate markable
if (is_atomic(mark, lex.atoms, lex) or
("@" in modifier_based_entity
and "_" not in mark_id)) and mark.end > mark.start:
for index in enumerate(mark_candidates_by_head):
key = index[1]
# Note that the key may contain underscores if it's a composite, but those can't be atomic
if key != mark.head.id and mark.start <= int(
re.sub('_.*', '',
key)) <= mark.end and '_' not in key:
if lex.filters["pronoun_pos"].match(
conll_tokens[int(re.sub('_.*', '',
key))].pos
) is None: # Make sure we're not removing a pronoun
keys_to_pop.append(key)
elif len(modifier_based_entity) > 1:
stoplist_prefix_tokens(mark, lex.entity_mods, keys_to_pop)
# Check for whole markable only exclusion
if mark.text + "@" in lex.stop_list:
keys_to_pop.append(mark_id)
for key in keys_to_pop:
mark_candidates_by_head.pop(key, None)
processed_marks = len(markables)
for mark_id in mark_candidates_by_head:
mark = mark_candidates_by_head[mark_id]
self.analyze_markable(mark, lex)
self.markcounter += 1
self.groupcounter += 1
this_markable = Markable(
"referent_" + str(self.markcounter), mark.head, mark.form,
mark.definiteness, mark.start, mark.end, mark.text,
mark.core_text, mark.entity, mark.entity_certainty,
mark.subclass, "new", mark.agree, mark.sentence, "none",
"none", self.groupcounter, mark.alt_entities,
mark.alt_subclasses, mark.alt_agree, mark.cardinality,
mark.submarks, mark.coordinate)
markables.append(this_markable)
markables_by_head[mark_id] = this_markable
markstart_dict[this_markable.start].append(this_markable)
markend_dict[this_markable.end].append(this_markable)
for current_markable in markables[processed_marks:]:
# DEBUG POINT
if current_markable.text == lex.debug["ana"]:
a = 5
if current_markable.text == lex.debug["ante"]:
pass
# Revise coordinate markable entities now that we have resolved all of their constituents
if len(current_markable.submarks) > 0:
assign_coordinate_entity(current_markable, markables_by_head)
if antecedent_prohibited(
current_markable, conll_tokens,
lex) or (current_markable.definiteness == "indef"
and lex.filters["apposition_func"].match(
current_markable.head.func) is None
and not lex.filters["allow_indef_anaphor"]):
antecedent = None
elif (current_markable.definiteness == "indef"
and lex.filters["apposition_func"].match(
current_markable.head.func) is not None
and not lex.filters["allow_indef_anaphor"]):
antecedent, propagation = find_antecedent(
current_markable, markables, lex, "appos")
else:
antecedent, propagation = find_antecedent(
current_markable, markables, lex)
if antecedent is not None:
if int(antecedent.head.id) < int(
current_markable.head.id) or 'invert' in propagation:
# If the rule specifies to invert
if 'invert' in propagation:
temp = antecedent
antecedent = current_markable
current_markable = temp
current_markable.antecedent = antecedent
current_markable.group = antecedent.group
# Check for apposition function if both markables are in the same sentence
if lex.filters["apposition_func"].match(current_markable.head.func) is not None and \
current_markable.sentence.sent_num == antecedent.sentence.sent_num:
current_markable.coref_type = "appos"
elif current_markable.form == "pronoun":
current_markable.coref_type = "ana"
elif current_markable.coref_type == "none":
current_markable.coref_type = "coref"
current_markable.infstat = "giv"
else: # Cataphoric match
current_markable.antecedent = antecedent
antecedent.group = current_markable.group
current_markable.coref_type = "cata"
current_markable.infstat = "new"
elif current_markable.form == "pronoun":
current_markable.infstat = "acc"
else:
current_markable.infstat = "new"
if current_markable.agree is not None and current_markable.agree != '':
lex.last[current_markable.agree] = current_markable
else:
pass