def __init__(
self,
resource_loader=None,
config=None,
allow_relaxed=True,
domain=None,
intent=None,
):
"""Initializes the parser
Args:
resource_loader (ResourceLoader): An object which can load
resources for the parser.
config (dict, optional): The configuration for the parser. If none
is provided the app config will be loaded.
"""
if not resource_loader and not config:
raise ValueError(
"Parser requires either a configuration or a resource loader")
app_path = resource_loader.app_path if resource_loader else None
try:
entity_types = path.get_entity_types(app_path) + ["unk"]
except TypeError:
entity_types = {"unk"}
self._resource_loader = resource_loader
self.config = get_parser_config(app_path, config, domain, intent) or {}
configured_entities = set()
for entity_type, entity_config in self.config.items():
configured_entities.add(entity_type)
configured_entities.update(entity_config.keys())
self._configured_entities = configured_entities
rules = generate_grammar(self.config, entity_types)
self._grammar = FeatureGrammar.fromstring(rules)
self._parser = FeatureChartParser(self._grammar)
if allow_relaxed:
relaxed_rules = generate_grammar(self.config,
entity_types,
relaxed=True)
self._relaxed_grammar = FeatureGrammar.fromstring(relaxed_rules)
self._relaxed_parser = FeatureChartParser(self._relaxed_grammar)
else:
self._relaxed_grammar = None
self._relaxed_parser = None
def main(argv):
"""
Loads grammar files from command-line args then parses lines from standard input.
"""
parser = argparse.ArgumentParser()
parser.add_argument(dest='grammars',
nargs='+',
help='Grammar file path(s)')
parser.add_argument('--draw',
dest='draw',
action='store_true',
help='Draw trees')
parser.add_argument('-v',
'--verbose',
dest='verbose',
action='store_true',
help='Be verbose')
args = parser.parse_args(argv)
grammar = load_grammars(args.grammars)
parser = FeatureChartParser(grammar,
trace=args.verbose,
trace_chart_width=80)
line = sys.stdin.readline()
while line:
if line[0] == '#': continue
tokens = line.lower().strip().split()
if len(tokens) == 0: continue
trees = list(parser.parse(tokens))
print('*** {} ***'.format(tokens))
if trees:
for tree in trees:
print(tree.pformat(margin=80))
if args.draw:
tree.draw()
# print(TreePrettyPrinter(tree).text())
else:
print('Could not parse {}'.format(tokens))
print('\n')
line = sys.stdin.readline()
def main():
ugrammar = FeatureGrammar.fromstring(rules)
uparser = FeatureChartParser(ugrammar)
index = 0
for sent in text:
index += 1
print_tree(sent, uparser, index)
print "Input testing sentece or the number of the above one: (q to quit)"
str = sys.stdin.readline().strip()
while str != "q":
try:
index = int(str)
print_tree(text[index], uparser, index)
except IndexError:
print "Index out of range. Please check."
except ValueError:
print_tree(str, uparser, -1)
print "Input testing sentece or the number of the above one: (q to quit)"
str = sys.stdin.readline().strip()
ADV -> 'always' | 'never' | 'intensely'
############## Preposition ################
P -> 'in' | 'before' | 'after' | 'when' | 'on' | 'beyond' | 'from' | 'to' | 'at'
######## Auxiliary #################
AUX[FORM=base, NUM=plur] -> 'do'
AUX[FORM=vbz, NUM=sing] -> 'does'
AUX[FORM=pret] -> 'did'
AUX[FORM=pastpart, NUM=sing] -> 'has'
AUX[FORM=pastpart, NUM=plur] -> 'have'
""")
uparser = FeatureChartParser(ugrammar)
text = """\
Bart laughs
Homer laughed
Bart and Lisa drink milk
Bart wears blue shoes
Lisa serves Bart a healthy green salad
Homer serves Lisa
Bart always drinks milk
Lisa thinks Homer thinks Bart drinks milk
Homer never drinks milk in the kitchen before midnight
when Homer drinks milk Bart laughs
when does Lisa drink the milk on the table
when do Lisa and Bart wear shoes
Bart thinks Lisa drinks milk on the table
import nltk
from nltk import FeatureChartParser
fcfg = nltk.data.load('P2.fcfg')
parser = FeatureChartParser(fcfg)
def parse_text(text):
examples = text.splitlines()
for sent in examples:
print(sent)
parses = parser.parse(sent.split())
for tree in parses:
print(tree)
def parse_file(name):
f = open(name, 'r')
text = f.read()
f.close()
parse_text(text)
print("================ Positive examples ================")
parse_file('P2.pos')
print("================ Negative examples ================")
parse_file('P2.neg')
def unification_grammar():
ugrammar = FeatureGrammar.fromstring("""\
################### RULES #################
S -> NP[NUM=?n] VP[NUM=?n]
S -> PREP_P S
S -> Wh_P AUX[NUM=?n] NP[NUM=?n] VP
NP[NUM=?n] -> ProperNoun[NUM=?n]
NP[NUM=?n] -> N[NUM=?n] | ADJ_P NP[NUM=?n] | DET[NUM=?n] NP[NUM=?n] | N[NUM=?n] PREP_P | ADJ_P
NP[NUM=?n] -> ProperNoun[NUM=?n] GER_P | GER_P
NP[NUM=pl] -> NP[NUM=?n] CC NP[NUM=?n]
VP[SUBCAT=?rest, NUM=?n] -> V[NUM=?n, SUBCAT=?rest] | VP[NUM=?n, TENSE=?t, SUBCAT=[HEAD=?arg, TAIL=?rest]] ARG[CAT=?arg]
VP[SUBCAT=?rest, NUM=?n] -> ADV_P V[NUM=?n, SUBCAT=?rest] | V[NUM=?n, SUBCAT=?rest] ADV_P
VP[SUBCAT=?rest, NUM=?n] -> MOD_P VP[TENSE=?t, SUBCAT=[HEAD=?arg, TAIL=?rest]] ARG[CAT=?arg]
VP[SUBCAT=?rest, NUM=?n] -> VTB[NUM=?n, SUBCAT=[HEAD=?arg, TAIL=?rest]] ARG[CAT=?arg]
VP[SUBCAT=?rest, NUM=?n] -> VTB VP[SUBCAT=?rest]
GER_P -> GER NP
ADJ_P -> ADJ | ADJ ADJ_P
ADV_P -> ADV | ADV ADV_P
PREP_P -> PREP NP | PREP S
MOD_P -> MOD AUX[NUM=pl] | MOD ADV AUX[NUM=pl]
Wh_P -> Wh | Wh ARG[CAT=?arg]
ARG[CAT=np] -> NP
ARG[CAT=pp] -> PREP_P
ARG[CAT=s] -> S
################# Lexicons #################
################## NOUN ###################
###########################################
ProperNoun[NUM=sg] -> 'Homer' | 'Bart' | 'Lisa'
N[NUM=sg] -> 'milk' | 'salad' | 'midnight' | 'kitchen' | 'table'
N[NUM=pl] -> 'shoes' | 'tables'
################# VERB ####################
###########################################
############### PRESENT ###################
#########----- Intransitive -----##########
V[TENSE=pres, NUM=sg, SUBCAT=nil]-> 'laughs' | 'smiles' | 'walks' | 'serves' | 'drinks'
V[TENSE=pres, NUM=pl, SUBCAT=nil] -> 'laugh' | 'smile' | 'walk' | 'serve' |'drink'
#########----- Transitive ------###########
V[TENSE=pres, NUM=sg, SUBCAT=[HEAD=s,TAIL=nil]] -> 'thinks' | 'believes'
V[TENSE=pres, NUM=pl, SUBCAT=[HEAD=s,TAIL=nil]] -> 'think' | 'believe'
V[TENSE=pres, NUM=sg, SUBCAT=[HEAD=np,TAIL=nil]] ->'serves' | 'drinks' | 'wears' | 'likes'
V[TENSE=pres, NUM=pl, SUBCAT=[HEAD=np,TAIL=nil]] ->'serve' | 'drink' | 'wear' | 'like'
V[TENSE=pres, NUM=sg, SUBCAT=[HEAD=pp,TAIL=nil]] ->'walks' | 'teaches'
V[TENSE=pres, NUM=pl, SUBCAT=[HEAD=pp,TAIL=nil]] ->'walk' | 'teach'
######### primary & secondary ########
V[TENSE=pres, NUM=sg, SUBCAT=[HEAD=np, TAIL=[HEAD=np,TAIL=nil]]] -> 'serves'
V[TENSE=pres, NUM=pl, SUBCAT=[HEAD=np, TAIL=[HEAD=np,TAIL=nil]]] -> 'serve'
V[TENSE=pres, NUM=pl, SUBCAT=[HEAD=s, TAIL=[HEAD=np,TAIL=nil]]] -> 'think' | 'believe'
################# Past ####################
#########----- Intransitive -----##########
V[TENSE=past, SUBCAT=nil] -> 'laughed' | 'smiled' | 'walked'
#########----- Transitive ------###########
V[TENSE=past, SUBCAT=[HEAD=np,TAIL=nil]] -> 'drank' | 'wore' | 'served'
V[TENSE=pastpart, SUBCAT=[HEAD=np,TAIL=nil]] ->'drunk' | 'worn' | 'served' | 'seen'
############### PRESENT CONT. #############
V[TENSE=prescon, FORM=prespart , SUBCAT=[HEAD=np,TAIL=nil]] -> 'drinking' | 'wearing'
V[TENSE=prescon, FORM=prespart , SUBCAT=[HEAD=pp,TAIL=nil]] -> 'drinking'
################ Determiner ###############
DET[NUM=sg] -> 'a' | 'the' | 'that'
DET[NUM=pl] -> 'the' | 'these' | 'those'
################ Conjunction ##############
CC -> 'and'
################## Modal ##################
MOD -> 'may'
################# Gerund #################
GER -> 'drinking'
############ Adverb & Adjective ############
ADJ -> 'blue' | 'healthy' | 'green' | 'same'
ADV -> 'always' | 'never' | 'not' | 'yesterday'
############## Preposition ##################
PREP -> 'in' | 'before' | 'when' | 'on'
AUX[NUM=sg] -> 'does' | 'has'
AUX[NUM=pl] -> 'do' | 'have'
VTB[NUM=sg] -> 'is'
VTB[NUM=pl] -> 'are'
Wh -> 'when' | 'what' | 'where' | 'whom'
""")
uparser = FeatureChartParser(ugrammar)
sents = text_extended.splitlines()
for sent in sents:
parses = uparser.parse(sent.split())
print(sent)
for tree in parses:
print(tree)
class Parser:
"""
A language parser which is used to extract relations between entities in a
given query and group related entities together.
The parser uses a context free grammar based on a configuration to generate
candidate entity groupings. Heuristics are then used to rank and select a
grouping.
This rule based parser will be helpful in many situations, but if you have
a sufficiently sophisticated entity hierarchy, you may benefit from using a
statistical approach.
Attributes:
config (dict): The parser config.
"""
def __init__(
self,
resource_loader=None,
config=None,
allow_relaxed=True,
domain=None,
intent=None,
):
"""Initializes the parser
Args:
resource_loader (ResourceLoader): An object which can load
resources for the parser.
config (dict, optional): The configuration for the parser. If none
is provided the app config will be loaded.
"""
if not resource_loader and not config:
raise ValueError(
"Parser requires either a configuration or a resource loader")
app_path = resource_loader.app_path if resource_loader else None
try:
entity_types = path.get_entity_types(app_path) + ["unk"]
except TypeError:
entity_types = {"unk"}
self._resource_loader = resource_loader
self.config = get_parser_config(app_path, config, domain, intent) or {}
configured_entities = set()
for entity_type, entity_config in self.config.items():
configured_entities.add(entity_type)
configured_entities.update(entity_config.keys())
self._configured_entities = configured_entities
rules = generate_grammar(self.config, entity_types)
self._grammar = FeatureGrammar.fromstring(rules)
self._parser = FeatureChartParser(self._grammar)
if allow_relaxed:
relaxed_rules = generate_grammar(self.config,
entity_types,
relaxed=True)
self._relaxed_grammar = FeatureGrammar.fromstring(relaxed_rules)
self._relaxed_parser = FeatureChartParser(self._relaxed_grammar)
else:
self._relaxed_grammar = None
self._relaxed_parser = None
def parse_entities(
self,
query,
entities,
all_candidates=False,
handle_timeout=True,
timeout=MAX_PARSE_TIME,
):
"""Determines groupings of entities for the given query.
Args:
query (Query): The query being parsed.
entities (list[QueryEntity]): The entities to find groupings for.
all_candidates (bool, optional): Whether to return all the entity candidates.
handle_timeout (bool, optional): False if an exception should be raised in the event of
a parsing times out. Defaults to True.
timeout (float, optional): The amount of time to wait for the parsing to complete.
By default this is set to MAX_PARSE_TIME. If None is passed, the passing will never
time out
Returns:
(tuple[QueryEntity]): An updated version of the entities collection passed in with \
their parent and children attributes set appropriately.
"""
if not self._configured_entities:
return entities
if not handle_timeout:
return self._parse(query,
entities,
all_candidates=all_candidates,
timeout=timeout)
try:
return self._parse(query,
entities,
all_candidates=all_candidates,
timeout=timeout)
except ParserTimeout:
logger.warning("Parser timed out parsing query %r", query.text)
return entities
def _parse(self, query, entities, all_candidates, timeout):
entity_type_count = defaultdict(int)
entity_dict = {}
tokens = [] # tokens to be parsed
# generate sentential form (assumes entities are sorted)
for entity in entities:
entity_type = entity.entity.type
role_type = entity.entity.role
if role_type:
# Append role type to entity type with - separator
entity_with_role_type = entity_type + "--" + role_type
if entity_with_role_type in self._configured_entities:
entity_type = entity_with_role_type
if entity_type not in self._configured_entities:
entity_type = "unk"
entity_id = "{}{}".format(entity_type,
entity_type_count[entity_type])
entity_type_count[entity_type] += 1
entity_dict[entity_id] = entity
tokens.append(entity_id)
logger.debug("Parsing sentential form: %r", " ".join(tokens))
start_time = time.time()
parses = []
for parse in self._parser.parse(tokens):
parses.append(parse)
if timeout is not None and (time.time() - start_time) > timeout:
raise ParserTimeout("Parsing took too long")
if not parses and self._relaxed_parser:
for parse in self._relaxed_parser.parse(tokens):
parses.append(parse)
if timeout is not None and (time.time() -
start_time) > MAX_PARSE_TIME:
raise ParserTimeout("Parsing took too long")
if not parses:
if all_candidates:
return []
return entities
ranked_parses = self._rank_parses(query, entity_dict, parses, timeout,
start_time)
if all_candidates:
return ranked_parses
# if we still have more than one, choose the first
entities = self._get_flat_entities(ranked_parses[0], entities,
entity_dict)
return tuple(sorted(entities, key=lambda e: e.span.start))
def _rank_parses(self,
query,
entity_dict,
parses,
timeout,
start_time=None):
start_time = start_time or time.time()
resolved = OrderedDict()
for parse in parses:
if timeout is not None and time.time() - start_time > timeout:
raise ParserTimeout("Parsing took too long")
resolved[self._resolve_parse(parse)] = None
filtered = (p for p in resolved.keys())
# Prefer parses with fewer groups
parses = list(sorted(filtered, key=len))
filtered = (p for p in parses if len(p) <= len(parses[0]))
# Prefer parses with minimal distance from dependents to heads
parses = list(
sorted(filtered,
key=lambda p: self._parse_distance(p, query, entity_dict)))
min_parse_dist = self._parse_distance(parses[0], query, entity_dict)
filtered = (
p for p in parses
if self._parse_distance(p, query, entity_dict) <= min_parse_dist)
# TODO: apply precedence
return list(filtered)
def _parse_distance(self, parse, query, entity_dict):
total_link_distance = 0
stack = list(parse)
while stack:
node = stack.pop()
head = entity_dict[node.id]
for dep in node.dependents or set():
if dep.dependents:
stack.append(dep)
continue
child = entity_dict[dep.id]
if child.token_span.start > head.token_span.start:
intra_entity_span = Span(head.token_span.end,
child.token_span.start)
else:
intra_entity_span = Span(child.token_span.end,
head.token_span.start)
link_distance = 0
for token in intra_entity_span.slice(query.text.split(" ")):
if token in self.config[node.type][
dep.type]["linking_words"]:
link_distance -= 0.5
else:
link_distance += 1
total_link_distance += link_distance
return total_link_distance
@staticmethod
def _get_flat_entities(parse, entities, entity_dict):
stack = [g.to_query_entity(entity_dict) for g in parse]
new_dict = {}
while stack:
entity = stack.pop()
new_dict[(entity.entity.type, entity.span.start)] = entity
for child in entity.children or ():
stack.append(child)
return [
new_dict.get((e.entity.type, e.span.start), e) for e in entities
]
@classmethod
def _resolve_parse(cls, node):
groups = set()
for child in node:
child_symbol = child.label()[TYPE_FEATURE]
if child_symbol in START_SYMBOLS:
groups.update(cls._resolve_parse(child))
else:
group = cls._resolve_group(child).freeze()
groups.add(group)
return frozenset(groups)
@classmethod
def _resolve_group(cls, node):
symbol = node.label()[TYPE_FEATURE]
if not symbol[0].isupper():
# this node is a generic entity of type {symbol}, its child is the terminal
return _EntityNode(symbol, node[0], None)
# if first char is capitalized, this is a group!
group_type = symbol.lower()
dependents = set()
for child in node:
child_symbol = child.label()[TYPE_FEATURE]
if child_symbol == symbol:
# this is the ancestor of this group
group = cls._resolve_group(child)
elif child_symbol == group_type:
# this is the root ancestor of this group
group = cls._resolve_group(child)
group = _EntityNode(group.type, group.id, set())
else:
dependents.add(cls._resolve_group(child).freeze())
group.dependents.update(dependents)
return group
V[FORM=?vbz] -> 'laughs' | 'wears' | 'serves' | 'drinks' | 'thinks' | 'does' | 'do'
V[FORM=?pret] -> 'laughed'
V[FORM=?base] -> 'drink' | 'wear' | 'laugh'
DET[NUM=?sg] -> 'a' | 'the'
Adjective[CAT=?des] -> 'healthy'
Adjective[CAT=?col] -> 'blue' | 'green'
Adverb[T=?des] -> 'always' | 'never' | 'before'
Preposition[T=?gen] -> 'in' | 'on'
NN[NUM=?sg] -> 'shoes' | 'salad' | 'milk' | 'kitchen' | 'midnight' | 'table'
NN[NUM=?pl] -> 'milk' | 'shoes'
ConjuctiveJoin[NUM=?pl] -> 'and'
ConjuctiveConditional[T=?con] -> 'when'
ConjuctiveConditional[T=?quest] -> 'when'
""")
grammar_feature_parser = FeatureChartParser(grammar_feature)
#'''
def _parse_and_print_unification(sents):
for i in range(len(sents)):
parses = grammar_feature_parser.parse(sents[i].split())
print("\r\n")
print "Sentence", i + 1, " : ",
print(sents[i])
for tree in parses:
print(tree)
_parse_and_print_unification(sents_split)
