def parse(text):
"""
Primary function to run syntaxnet and PredPatt over input sentences.
"""
parse_tree, trace = annotate_text(text)
conll_parsed = parse_to_conll(parse_tree)
conll_pp = [ud_parse for sent_id, ud_parse in load_conllu(conll_parsed)][0]
#PredPatt options. Modify as needed.
resolve_relcl = True # relative clauses
resolve_appos = True # appositional modifiers
resolve_amod = True # adjectival modifiers
resolve_conj = True # conjuction
resolve_poss = True # possessives
ud = dep_v2.VERSION # the version of UD
opts = PredPattOpts(resolve_relcl=resolve_relcl,
resolve_appos=resolve_appos,
resolve_amod=resolve_amod,
resolve_conj=resolve_conj,
resolve_poss=resolve_poss,
ud=ud)
ppatt = PredPatt(conll_pp, opts=opts)
predicate_deps, arg_deps = get_ud_fragments(ppatt)
#NOTE:
#This returns the pretty print formatted string from PredPatt. This is done
#largely as a place holder for JSON compatability within the REST API.
return {'predpatt': {'predicate_deps': predicate_deps,
'arg_deps': arg_deps},
'conll': conll_parsed,
'original': text}
def extract_triples(input_remaining, params):
opts = PredPattOpts(
resolve_relcl=True, # relative clauses
resolve_appos=True, # appositional modifiers
resolve_amod=True, # adjectival modifiers
resolve_conj=True, # conjuction
resolve_poss=True, # possessives
ud=dep_v1.VERSION, # the version of UD
)
triples = {}
remaining = {}
for idx in input_remaining:
for line in input_remaining[idx]:
if line.strip():
try:
pp = PredPatt.from_sentence(line,
opts=opts,
cacheable=False)
extractions = get_predpatt_triples(pp, line)
if extractions:
triples.setdefault(idx, []).extend(extractions)
except KeyError:
pass
if idx not in triples:
remaining[idx] = input_remaining[idx]
triples[idx] = []
return triples, remaining
def setup_graph():
ud = DependencyGraphBuilder.from_conll(listtree, 'tree1')
pp = PredPatt(next(load_conllu(rawtree))[1],
opts=PredPattOpts(resolve_relcl=True,
borrow_arg_for_relcl=True,
resolve_conj=False,
cut=True))
graph = PredPattGraphBuilder.from_predpatt(pp, ud, 'tree1')
return pp, graph
def extract_predpatt(path='../../data/corpora/ud/UD_English-EWT-r1.2/'):
'''
Extract PredPatt objects from CONLLU files
'''
options = PredPattOpts(resolve_relcl=True,
borrow_arg_for_relcl=True,
resolve_conj=False,
cut=True) # Resolve relative clause
patt = {}
for file in os.listdir(path):
if file.endswith('.conllu'):
with open(path + file, 'r') as infile:
for sent_id, ud_parse in load_conllu(infile.read()):
patt[file + " " + sent_id] = \
PredPatt(ud_parse, opts=options)
return patt
def __init__(
self,
path_to_udpipe: str,
resolve_relcl: bool = True,
resolve_appos: bool = True,
resolve_amod: bool = True,
resolve_conj: bool = True,
resolve_poss: bool = True,
ud=dep_v2.VERSION,
):
super().__init__()
self.model = Model.load(path_to_udpipe)
self.pipeline = Pipeline(
self.model, "tokenize", Pipeline.DEFAULT, Pipeline.DEFAULT, "conllu"
)
self._error = ProcessingError()
self._opts = PredPattOpts(
resolve_relcl=resolve_relcl,
resolve_appos=resolve_appos,
resolve_amod=resolve_amod,
resolve_conj=resolve_conj,
resolve_poss=resolve_poss,
ud=ud,
)
from collections import defaultdict, Counter
from itertools import product
from tqdm import tqdm
#import matplotlib.pyplot as plt
import re
import json
import argparse
from predpatt import load_conllu
from predpatt import PredPatt
from predpatt import PredPattOpts
from nltk import DependencyGraph
import re
from recast_utils import *
options = PredPattOpts(resolve_relcl=True,
borrow_arg_for_relcl=True,
resolve_conj=False,
cut=True)
#Data Locations:
ud_path = "../../data/raw_data/UD_English-EWT-r1.3/"
ud_train = "../../data/raw_data/UD_English-EWT-r1.3/en-ud-train.conllu"
ud_dev = "../../data/raw_data/UD_English-EWT-r1.3/en-ud-dev.conllu"
ud_test = "../../data/raw_data/UD_English-EWT-r1.3/en-ud-test.conllu"
ud_data = [ud_train, ud_dev, ud_test]
# #### Hypothesis Generation Functions
duration_dict = defaultdict(str)
duration_dict[0] = 'instantaneously'
duration_dict[1] = 'second'
duration_dict[2] = 'minute'
duration_dict[3] = 'hour'
print ' ', a, a.phrase()
# Uncomment to list rules which fired on this proposition. Along with
# an explanation.
#for r in a.rules:
# print ' %s: %s' % (r, r.explain())
print '______________________________________________________________________________'
print
# To change certain behaviors, you can pass different options for the PredPatt
# instance. For example, to disable expansion of conjunctions and extraction of
# amods, use the following:
from predpatt import PredPattOpts
P = PredPatt.from_sentence(sentence,
opts=PredPattOpts(resolve_amod=0, resolve_conj=0))
print P.pprint(color=1)
print '______________________________________________________________________________'
print
#______________________________________________________________________________
# Bonus material
# Already have a constituency parse? No problem!
P = PredPatt.from_constituency(
'( (S (NP (NNP Chris)) (VP (VBZ loves) (NP (NNP Pat))) (. .)) )')
print P.pprint(track_rule=True, color=True)
print '______________________________________________________________________________'
# pylint: disable=W0221
# pylint: disable=R0903
# pylint: disable=R1704
"""Module for converting PredPatt objects to networkx digraphs"""
from os.path import basename, splitext
from typing import Tuple, Hashable, TextIO, Optional, Union
from networkx import DiGraph
from predpatt import load_conllu, PredPatt, PredPattOpts
from ..corpus import Corpus
from ..syntax.dependency import CoNLLDependencyTreeCorpus
DEFAULT_PREDPATT_OPTIONS = PredPattOpts(resolve_relcl=True,
borrow_arg_for_relcl=True,
resolve_conj=False,
cut=True) # Resolve relative clause
class PredPattCorpus(Corpus):
"""Container for predpatt graphs"""
def _graphbuilder(self,
graphid: Hashable,
predpatt_depgraph: Tuple[PredPatt, DiGraph]) -> DiGraph:
"""
Parameters
----------
treeid
an identifier for the tree
predpatt_depgraph
a pairing of the predpatt for a dependency parse and the graph
def main():
patterns = ''
sentence = 'The quick brown fox jumped over the lazy dog .'
tags = ''
parse = ''
if request.GET.get('sentence', '').strip():
sentence = request.GET.get('sentence', '').strip()
pp_opts = PredPattOpts()
for k, v in sorted(PredPattOpts().__dict__.iteritems()):
v = int(float(request.GET.get(
k, v))) # all options are true/false for now.
setattr(pp_opts, k, v)
if sentence:
#for sent in sent_detector.tokenize('"John saw Mary", said Jason. Larry met Sally for dinner.'):
# print tokenize(sent)
original_sentence = sentence
parse = parser(sentence, tokenized=False)
P = PredPatt(parse, opts=pp_opts)
patterns = P.pprint(track_rule=True)
tags = ' '.join('%s/%s' % x for x in zip(parse.tokens, parse.tags))
parse = parse.pprint(K=3)
# remove predpatt's bracketed comments
patterns = re.sub(r'\s*\[.*?\]', '', patterns)
patterns = dedent(patterns)
opts = []
for k, v in sorted(pp_opts.__dict__.iteritems()):
# Create a hidden textbox with the false value because the values of
# "unchecked" boxes don't get posted with form.
opts.append('<input type="hidden" value="0" name="%s">' % (k, ))
opts.append('<input type="checkbox" name="%s" value="1" %s> %s<br/>' %
(k, 'checked' if v else '', k))
options = '\n'.join(opts)
return template("""
<html>
<head>
<!-- JQuery -->
<script src="//code.jquery.com/jquery-2.1.4.min.js"></script>
<!-- Bootstrap -->
<link rel="stylesheet" href="//maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css"/>
<link rel="stylesheet" href="//maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap-theme.min.css"/>
<script src="//maxcdn.bootstrapcdn.com/bootstrap/3.3.1/js/bootstrap.min.js"></script>
<!-- Chosen Dropdown Library -->
<link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/chosen/1.4.2/chosen.css"/>
<script src="//cdnjs.cloudflare.com/ajax/libs/chosen/1.4.2/chosen.jquery.min.js"></script>
<style>
html {
overflow: -moz-scrollbars-vertical;
overflow: scroll;
}
</style>
</head>
<body>
<div style="width: 800px; padding: 10px; margin-left: auto; margin-right: auto;">
<h1>PredPatt</h1>
<strong>Sentence</strong>
<pre>{{sentence}}</pre>
<strong>Propositions</strong>
<div id="propositions">
<pre>
{{patterns}}
</pre>
<div>
<button class="btn" data-toggle="collapse" data-target="#parse" style="margin-bottom: 10px;">Toggle Parse</button>
<div id="parse" class="collapse">
<strong>Tags</strong>
<pre>
{{tags}}
</pre>
<strong>Parse</strong>
<pre>
{{parse}}
</pre>
</div>
</div>
<strong>Input</strong>
<form action="/" method="GET">
<textarea type="text" name="sentence" style="height:50px; width: 100%;"
placeholder="e.g., The quick brown fox jumped over the lazy dog."
class="form-control"
autofocus>{{original_sentence}}</textarea>
<div style="padding: 10px;"><strong>Options</strong><br/>""" + options + """
</div>
<br/>
<input type="submit" name="save" value="submit">
</form>
</div>
</body>
</html>
""",
sentence=sentence,
original_sentence=original_sentence,
patterns=patterns,
tags=tags,
parse=parse,
options=options)
def hand_engineering(prot, batch_size, data, data_dev):
'''
Hand engineered feature extraction. Supports the following - UD,
Verbnet classids, Wordnet supersenses, concreteness ratings, LCS
eventivity scores
'''
home = expanduser("~")
framnet_posdict = {
'V': 'VERB',
'N': 'NOUN',
'A': 'ADJ',
'ADV': 'ADV',
'PREP': 'ADP',
'NUM': 'NUM',
'INTJ': 'INTJ',
'ART': 'DET',
'C': 'CCONJ',
'SCON': 'SCONJ',
'PRON': 'PRON',
'IDIO': 'X',
'AVP': 'ADV'
}
# Load the features
features = {}
with open(home + '/Desktop/protocols/data/features-2.tsv', 'r') as f:
for line in f.readlines():
feats = line.split('\t')
features[feats[0]] = (feats[1].split(), feats[2].split())
# Load the predpatt objects for creating features
files = [
'/Downloads/UD_English-r1.2/en-ud-train.conllu',
'/Downloads/UD_English-r1.2/en-ud-dev.conllu',
'/Downloads/UD_English-r1.2/en-ud-test.conllu'
]
home = expanduser("~")
options = PredPattOpts(resolve_relcl=True,
borrow_arg_for_relcl=True,
resolve_conj=False,
cut=True) # Resolve relative clause
patt = {}
for file in files:
path = home + file
with open(path, 'r') as infile:
for sent_id, ud_parse in load_conllu(infile.read()):
patt[file[33:][:-7] + " " + sent_id] = PredPatt(ud_parse,
opts=options)
data['Structure'] = data['Split.Sentence.ID'].map(lambda x:
(patt[x], features[x]))
data_dev['Structure'] = data_dev['Split.Sentence.ID'].map(
lambda x: (patt[x], features[x]))
raw_x = data['Structure'].tolist()
raw_dev_x = data_dev['Structure'].tolist()
all_x = raw_x + raw_dev_x
all_feats = '|'.join(['|'.join(all_x[i][1][0]) for i in range(len(all_x))])
feature_cols = Counter(all_feats.split('|'))
# All UD dataset features
all_ud_feature_cols = list(
feature_cols.keys()) + [(a + "_dep") for a in feature_cols.keys()]
# Concreteness
f = open(home + '/Desktop/protocols/data/concrete.pkl', 'rb')
concreteness = pickle.load(f)
if prot == 'arg':
conc_cols = ['concreteness']
else:
conc_cols = ['concreteness', 'max_conc', 'min_conc']
f.close()
# LCS eventivity
from lcsreader import LexicalConceptualStructureLexicon
lcs = LexicalConceptualStructureLexicon(
home + '/Desktop/protocols/data/verbs-English.lcs')
lcs_feats = ['lcs_eventive', 'lcs_stative']
# Wordnet supersenses(lexicographer names)
supersenses = list(
set(['supersense=' + x.lexname() for x in wordnet.all_synsets()]))
# Framenet
lem2frame = {}
for lm in framenet.lus():
for lemma in lm['lexemes']:
lem2frame[lemma['name'] + '.' +
framnet_posdict[lemma['POS']]] = lm['frame']['name']
frame_names = ['frame=' + x.name for x in framenet.frames()]
# Verbnet classids
verbnet_classids = ['classid=' + vcid for vcid in verbnet.classids()]
# Lexical features
lexical_feats = [
'can', 'could', 'should', 'would', 'will', 'may', 'might', 'must',
'ought', 'dare', 'need'
] + [
'the', 'an', 'a', 'few', 'another', 'some', 'many', 'each', 'every',
'this', 'that', 'any', 'most', 'all', 'both', 'these'
]
dict_feats = {}
for f in verbnet_classids + lexical_feats + supersenses + frame_names + lcs_feats + all_ud_feature_cols + conc_cols:
dict_feats[f] = 0
x_pd = pd.DataFrame([
features_func(sent_feat=sent,
token=token,
lemma=lemma,
dict_feats=dict_feats.copy(),
prot=prot,
concreteness=concreteness,
lcs=lcs,
l2f=lem2frame) for sent, token, lemma in
zip(raw_x, data['Root.Token'].tolist(), data['Lemma'].tolist())
])
dev_x_pd = pd.DataFrame([
features_func(sent_feat=sent,
token=token,
lemma=lemma,
dict_feats=dict_feats.copy(),
prot=prot,
concreteness=concreteness,
lcs=lcs,
l2f=lem2frame)
for sent, token, lemma in zip(raw_dev_x, data_dev['Root.Token'].tolist(
), data_dev['Lemma'].tolist())
])
# Figure out which columns to drop(they're always zero)
todrop1 = dev_x_pd.columns[(dev_x_pd == 0).all()].values.tolist()
todrop = x_pd.columns[(x_pd == 0).all()].values.tolist()
intdrop = [a for a in todrop if a not in todrop1]
cols_to_drop = cols_to_drop = list(set(todrop) - set(intdrop))
x = x_pd.drop(cols_to_drop, axis=1).values.tolist()
dev_x = dev_x_pd.drop(cols_to_drop, axis=1).values.tolist()
x = [[a[:] for a in x[i:i + batch_size]]
for i in range(0, len(data), batch_size)]
dev_x = [[a[:] for a in dev_x[i:i + batch_size]]
for i in range(0, len(data_dev), batch_size)]
return x, dev_x
"""
Documentation test runner.
"""
from __future__ import print_function
import re, codecs
from predpatt import PredPatt, PredPattOpts, Parser
from termcolor import colored
ppattopts = PredPattOpts(simple=False,
cut=False,
resolve_relcl=True,
resolve_appos=True,
resolve_amod=True,
resolve_conj=True,
resolve_poss=True,
borrow_arg_for_relcl=True,
big_args=False,
ud="1.0")
def test():
from argparse import ArgumentParser
p = ArgumentParser()
p.add_argument('--filename', default='doc/DOCTEST.md')
args = p.parse_args()
sentences = re.findall(
'^> (.*)\n([\w\W]*?)(?=^>|<END>)',
codecs.open(args.filename, encoding='utf-8').read() + '\n<END>',
