def parse_using_stanfordparser(tokenized_sent,
display_tree=False,
printNP=False,
printLeave=False):
result = stanford_parser.tagged_parse(tokenized_sent)
for item in result:
# print item
if display_tree:
Tree.draw(item)
if printNP:
NPs = list(
Tree.subtrees(
item,
filter=lambda x: x.label() == 'NP' and x.height() <= 6))
for n in NPs:
if printLeave:
candidate = Tree.leaves(n)
s = ' '.join(candidate)
if len(candidate) == 1:
if re.search(re.compile(r'[A-Z_-]+', re.X), s):
print s
else:
print s
else:
tags = []
for t in Tree.subtrees(n):
if t.label() not in ['NP', 'S', 'VP']:
tags.append(t.label())
tagged = []
for w in range(len(Tree.leaves(n))):
tagged.append(
(Tree.leaves(n)[w], tags[w].encode('gbk')))
regexp_ner_m2(regexp_grammar, tagged)
def tree():
from nltk.tree import Tree
print(Tree(1, [2, Tree(3, [4]), 5]))
vp = Tree('VP', [Tree('V', ['saw']), Tree('NP', ['him'])])
s = Tree('S', [Tree('NP', ['I']), vp])
print(s)
s.draw()
def visualize_sentence_tree(sentence_tree):
processed_tree = process_sentence_tree(sentence_tree)
processed_tree = [
Tree(item[0], [Tree(x[1], [x[0]]) for x in item[1]])
for item in processed_tree
]
tree = Tree('S', processed_tree)
tree.draw()
def visualize_sentence_tree(sentence_tree):
processed_tree = process_sentence_tree(sentence_tree)
processed_tree = [
Tree( item[0],
[
Tree(x[1], [x[0]])
for x in item[1]
]
)
for item in processed_tree
]
tree = Tree('S', processed_tree )
tree.draw()
def translate(tree: Tree, translation_rules: list, draw=False):
put_left = list(
filter(
lambda i: isinstance(tree[i], Tree) and tree[i].label() in
translation_rules and not 'put_left' in locals(),
tree.treepositions()))[0] if len(tree) != 0 else []
if len(put_left) != 0:
tree = apply_translation(put_left, tree)
if tree[tree.treepositions()[-2]].label() not in [
Nonterminal("AUX"), Nonterminal("VERB")
]:
tree = apply_translation(tree.treepositions()[-2], tree)
if draw: tree.draw()
return tree
def read(parser,
sentence=None,
pos=None,
critical=None,
actions=actions,
blind_actions=actions,
word_freq=word_freq,
label_freq=label_freq,
strength_of_association={},
decmem={},
lexical=True,
visual=True,
syntactic=True,
reanalysis=True,
prints=True):
"""
Read a sentence.
:param sentence: what sentence should be read (list).
:param pos: what pos should be used (list, matching in length with sentence).
:param actions: dataframe of actions
:param lexical - should lexical information affect reading time?
:param visual - should visual information affect reading time?
:param syntactic - should syntactic information affect reading time?
:param reanalysis - should reanalysis of parse affect reading time?
"""
parser.set_decmem(decmem) # TODO: dont remove??
tobe_removed = {
i
for i in range(len(sentence))
if (re.match("[:]+", sentence[i]) or sentence[i] == "'s")
and i != len(sentence) - 1
} #remove what is not a full word
print(sentence)
for x in tobe_removed:
print(sentence[x])
critical_rules = dict()
# for critical sentences you can assume that specific rules apply to ensure that parsing of gp sentences proceeds correctly
# for example for sentence 'the horse raced past the barn fell', the following would work on the noun horse (uncomment if using)
#critical_rules = {'1': [['reduce_unary', 'NP_BAR'], ['reduce_binary', 'NP'], ['shift', "''"]]}
if not lexical:
for x in parser.decmem:
parser.decmem.activations[
x] = 100 #this is to nulify the effect of word retrieval to almost 0
parser.retrievals = {}
parser.set_retrieval("retrieval")
parser.visbuffers = {}
parser.goals = {}
parser.set_goal("g")
parser.set_goal(name="imaginal", delay=0)
parser.set_goal(name="imaginal_reanalysis", delay=0)
parser.set_goal("word_info")
stimuli = [{} for i in range(len(sentence))]
pos_word = 10
environment.current_focus = (pos_word + 7 +
7 * visual_effect(sentence[0], visual), 180)
pos_words = []
for x in range(41):
#this removes any move eyes created previously; we assume that no sentence is longer than 20 words
parser.productionstring(name="move eyes" + str(x),
string="""
=g>
isa reading
state dummy
==>
=g>
isa reading
state dummy""")
# create fixed rules for eye movements
for i, word in enumerate(sentence):
pos_word += 7 + 7 * visual_effect(word, visual)
pos_words.append((pos_word, 180))
for j in range(len(stimuli)):
stimuli[j].update({
i: {
'text': word,
'position': (pos_word, 180),
'vis_delay': visual_effect(word, visual)
}
})
if i < len(sentence) - 3:
parser.productionstring(name="move eyes" + str(i),
string="""
=g>
isa reading
state move_eyes
position """ + str(i) + """
?manual>
preparation free
processor free
==>
=imaginal>
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(sentence[i + 2]) + '"' + """
WORD_NEXT0_POS """ + str(pos[i + 2]) + """
=g>
isa reading
state reading_word
position """ + str(i + 1) + """
tag """ + str(pos[i + 1]) + """
?visual_location>
attended False
+visual_location>
isa _visuallocation
screen_x """ + str(pos_word + 7 +
7 * visual_effect(sentence[i + 1], visual)) + """
screen_y 180
~visual>""")
elif i < len(sentence) - 2:
parser.productionstring(name="move eyes" + str(i),
string="""
=g>
isa reading
state move_eyes
position """ + str(i) + """
?manual>
preparation free
processor free
==>
=imaginal>
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(sentence[i + 2]) + '"' + """
WORD_NEXT0_POS """ + str(pos[i + 2]) + """
=g>
isa reading
state reading_word
position """ + str(i + 1) + """
tag """ + str(pos[i + 1]) + """
?visual_location>
attended False
+visual_location>
isa _visuallocation
screen_x """ + str(pos_word + 7 +
7 * visual_effect(sentence[i + 1], visual)) + """
screen_y 180
~visual>""")
elif i < len(sentence) - 1:
parser.productionstring(name="move eyes" + str(i),
string="""
=g>
isa reading
state move_eyes
position """ + str(i) + """
?manual>
preparation free
==>
=imaginal>
isa action_chunk
WORD_NEXT0_LEX None
=g>
isa reading
state reading_word
position """ + str(i + 1) + """
tag """ + str(pos[i + 1]) + """
?visual_location>
attended False
+visual_location>
isa _visuallocation
screen_x """ + str(pos_word + 7 +
7 * visual_effect(sentence[i + 1], visual)) + """
screen_y 180
~visual>""")
if prints:
print(sentence)
parser.goals["g"].add(
actr.chunkstring(string="""
isa reading
state reading_word
position 0
tag """ + str(pos[0])))
parser.goals["imaginal"].add(
actr.chunkstring(string="""
isa action_chunk
TREE1_LABEL NOPOS
TREE1_HEAD noword
TREE2_LABEL xxx
TREE2_HEAD xxx
TREE3_LABEL xxx
TREE3_HEAD xxx
ANTECEDENT_CARRIED NO
WORD_NEXT0_LEX """ + '"' + str(sentence[1]) + '"' + """
WORD_NEXT0_POS """ + str(pos[1])))
# start a dictionary that will collect all created structures, and a list of built constituents
constituents = {}
built_constituents = [(Tree("xxx", []), (None, "xxx")),
(Tree("xxx", []), (None, "xxx")),
(Tree("NOPOS", []), (None, "noword"))]
final_tree = Tree("X", [])
if prints:
parser_sim = parser.simulation(
realtime=False,
gui=False,
trace=True,
environment_process=environment.environment_process,
stimuli=stimuli,
triggers='space',
times=40)
else:
parser_sim = parser.simulation(
realtime=False,
gui=True,
trace=False,
environment_process=environment.environment_process,
stimuli=stimuli,
triggers='space',
times=40)
antecedent_carried = "NO"
what_antecedent_carried = None
eyemove_times = [] #reaction times per word
reanalysis_list, words_list, activations_list, agreeing_actions_list, matching_fs_list, total_fan_list, actions_list = [], [], [], [], [], [], [] #collects total activation of rules, agreeing_actions... per sentence (used to find out what plays a role in syntactic parsing for RTs)
wh_gaps_list = []
word_parsed = 0
last_time = 0
activations, agreeing_actions, matching_fs, total_fan = [], [], [], [
] #collects total activation of rules, agreeing_actions... per word (used to find out what plays a role in syntactic parsing for RTs)
retrieve_wh_reanalysis = None
while True:
try:
parser_sim.step()
#print(parser_sim.current_event)
except simpy.core.EmptySchedule:
eyemove_times = [
10 for _ in sentence
] #if sth goes wrong, it's probably because it got stuck somewhere; in that case report time-out time per word (40 s) or nan
break
if parser_sim.show_time() > 60:
eyemove_times = [
10 for _ in sentence
] #this takes care of looping or excessive time spent - break if you loop (40 s should be definitely enough to move on)
break
if re.search("^SHIFT COMPLETE", str(parser_sim.current_event.action)):
current_word_focused = pos_words.index(
tuple(environment.current_focus))
extra_rule_time = parser.model_parameters[
"latency_factor"] * np.exp(
-parser.model_parameters["latency_exponent"] *
np.mean(activations) / 10)
# two things play a role - number of matching features; fan of each matching feature; explore these two separately
if len(eyemove_times) not in tobe_removed:
eyemove_times.append(parser_sim.show_time() + extra_rule_time -
last_time)
else:
tobe_removed.remove(len(eyemove_times))
for i in range(word_parsed + 1, current_word_focused):
eyemove_times.append(0)
#eyemove_times.append((parser_sim.show_time() + extra_rule_time - last_time)/(current_word_focused-word_parsed))
last_time = parser_sim.show_time()
word_parsed = current_word_focused
if word_parsed >= len(sentence):
if len(eyemove_times) not in tobe_removed:
eyemove_times.append(parser_sim.show_time() - last_time)
break
#this below implements carrying out an action
if re.search("^RULE FIRED: recall action", parser_sim.current_event.action) or\
re.search("^RULE FIRED: move to last action", parser_sim.current_event.action):
postulated_gaps, reduced_unary = 0, 0
postulate_gaps, reduce_unary = True, True
parser_sim.steps(2) #exactly enough steps to make imaginal full
if prints:
print(parser.goals["imaginal"])
#add new word to the list of used words
built_constituents.append(
(Tree(
str(parser.goals["imaginal"].copy().pop().TREE0_LABEL),
(str(parser.goals["imaginal"].copy().pop().TREE0_HEAD), )),
(None,
str(parser.goals["imaginal"].copy().pop().TREE0_HEAD))))
built_constituents_reanalysis = built_constituents.copy()
parser.goals["imaginal_reanalysis"].add(
parser.goals["imaginal"].copy().pop())
recently_retrieved = set()
#set retrieve_wh to None or, if the reanalysis already postulated a gap, to "yes"
retrieve_wh = retrieve_wh_reanalysis
retrieve_wh_reanalysis = None
activations, agreeing_actions, matching_fs, total_fan = [], [], [], [
] #collects total activation of rules, agreeing_actions... (used to find out what plays a role in syntactic parsing for RTs)
#antecedent_carried temporarily updated in the blind analysis; we will record the original position, which is the non-temporary one, in antecedent_carried_origo and re-use it after the blind analysis; what_antecedent_carried - specifies the category of antecedent
antecedent_carried_origo = antecedent_carried
first_action = True
if word_parsed not in tobe_removed:
reanalysis_list.append("no") #by dft no reanalysis recorded
#this loop for actual blind analysis
while True:
parser_retrievals, number_of_agreeing_actions, number_of_matching_fs, fan_size = ut.recall_action(
blind_actions,
parser.goals["imaginal"],
parser.goals["word_info"],
None,
recently_retrieved,
built_constituents,
word_freq,
label_freq,
prints=False,
strength_of_association=strength_of_association,
postulate_gaps=postulate_gaps,
reduce_unary=reduce_unary,
blind={"WORD_NEXT0_LEX", "WORD_NEXT0_POS"})
# the activation for the first word comes only from the blind
if word_parsed == 0:
activations.append(parser_retrievals[0])
agreeing_actions.append(number_of_agreeing_actions)
matching_fs.append(number_of_matching_fs)
total_fan.append(fan_size)
ut.collect_parse(parser_retrievals[1], built_constituents)
tree0_label = built_constituents[-1][0].label()
tree1_label = built_constituents[-2][0].label()
tree2_label = built_constituents[-3][0].label()
tree3_label = built_constituents[-4][0].label()
children = {
"".join(["tree", str(x)]): ["NOPOS", "NOPOS"]
for x in range(4)
}
for x, subtree in enumerate(built_constituents[-1][0]):
if isinstance(subtree,
Tree) and subtree.label() != ut.EMPTY:
children["tree0"][x] = subtree.label()
if re.search("_BAR", children["tree0"][1]):
if built_constituents[-1][0][1][1].label(
) == ut.EMPTY or re.search(
"_BAR", built_constituents[-1][0][1][1].label()):
children["tree0"][1] = built_constituents[-1][0][1][
0].label()
else:
children["tree0"][1] = built_constituents[-1][0][1][
1].label()
for x, subtree in enumerate(built_constituents[-2][0]):
if isinstance(subtree,
Tree) and subtree.label() != ut.EMPTY:
children["tree1"][x] = subtree.label()
if re.search("_BAR", children["tree1"][1]):
if built_constituents[-2][0][1][1].label(
) == ut.EMPTY or re.search(
"_BAR", built_constituents[-2][0][1][1].label()):
children["tree1"][1] = built_constituents[-2][0][1][
0].label()
else:
children["tree1"][1] = built_constituents[-2][0][1][
1].label()
# block looping through reduce_unary (at most 2 reduce_unary allowed)
if parser_retrievals[1] and parser_retrievals[1][
"action"] == 'reduce_unary':
reduced_unary += 1
if reduced_unary == 2:
reduce_unary = False
reduced_unary = 0
else:
reduced_unary = 0
reduce_unary = True
if parser_retrievals[1] and parser_retrievals[1][
"action"] == 'postulate_gap':
if antecedent_carried == "YES" and syntactic and re.search(
"t",
str(parser_retrievals[1]["action_result_label"]
[0])):
retrieve_wh = "yes"
if re.search(
"t",
str(parser_retrievals[1]["action_result_label"]
[0])):
antecedent_carried = "NO"
#at most 3 gaps allowed
if postulated_gaps > 1:
postulate_gaps = False
postulated_gaps += 1
ci = parser.goals["imaginal"].pop()
string = """
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(ci.WORD_NEXT0_LEX) + '"' + """
WORD_NEXT0_POS '""" + str(ci.WORD_NEXT0_POS) + """'
ANTECEDENT_CARRIED """ + antecedent_carried + """
TREE0_HEAD """ + '"' + str(parser_retrievals[1]
["action_result_label"][0]) + '"' + """
TREE0_LEFTCHILD """ + children["tree0"][0] + """
TREE0_RIGHTCHILD """ + children["tree0"][1] + """
TREE0_LABEL '-NONE-'
TREE1_LEFTCHILD """ + children["tree1"][0] + """
TREE1_RIGHTCHILD """ + children["tree1"][1] + """
TREE0_HEADPOS """ + str(built_constituents[-1][1][0]) + """
TREE1_LABEL """ + '"' + tree1_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents[-2][1][0]) + """
TREE1_HEAD """ + '"' + str(built_constituents[-2][1][1]) + '"' + """
TREE2_LABEL """ + '"' + tree2_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents[-3][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents[-3][1][1]) + '"' + """
TREE3_LABEL """ + '"' + tree3_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents[-4][1][1]) + '"' + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal"].add(
actr.chunkstring(string=string))
parser.goals["word_info"].add(
actr.chunkstring(string="""
isa word
form '""" + str(parser_retrievals[1]
["action_result_label"][0]) + """'
cat '-NONE-'"""))
elif parser_retrievals[1]:
ci = parser.goals["imaginal"].pop()
string = """
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(ci.WORD_NEXT0_LEX) + '"' + """
WORD_NEXT0_POS '""" + str(ci.WORD_NEXT0_POS) + """'
ANTECEDENT_CARRIED """ + antecedent_carried + """
TREE0_LABEL """ + '"' + str(
built_constituents[-1][0].label()) + '"' + """
TREE0_HEADPOS """ + str(built_constituents[-1][1][0]) + """
TREE0_HEAD """ + '"' + str(built_constituents[-1][1][1]) + '"' + """
TREE0_LEFTCHILD """ + children["tree0"][0] + """
TREE0_RIGHTCHILD """ + children["tree0"][1] + """
TREE1_LABEL """ + '"' + tree1_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents[-2][1][0]) + """
TREE1_HEAD """ + '"' + str(built_constituents[-2][1][1]) + '"' + """
TREE1_LEFTCHILD """ + children["tree1"][0] + """
TREE1_RIGHTCHILD """ + children["tree1"][1] + """
TREE2_LABEL """ + '"' + tree2_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents[-3][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents[-3][1][1]) + '"' + """
TREE3_LABEL """ + '"' + tree3_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents[-4][1][1]) + '"' + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal"].add(
actr.chunkstring(string=string))
else:
break
if parser_retrievals[1]["action"] == 'shift':
#sometimes the parser would stop at BAR and shift; in reality, this is not possible since BARs are artificial categories
if re.search("_BAR", built_constituents[-1][0].label()):
built_constituents[-1][0].set_label(
re.split("_BAR",
built_constituents[-1][0].label())[0])
ci = parser.goals["imaginal"].pop()
string = """
isa action_chunk
TREE1_LABEL """ + '"' + tree0_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents[-1][1][0]) + """
TREE1_HEAD """ + '"' + str(built_constituents[-1][1][1]) + '"' + """
TREE1_LEFTCHILD """ + children["tree0"][0] + """
TREE1_RIGHTCHILD """ + children["tree0"][1] + """
TREE2_LABEL """ + '"' + tree1_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents[-2][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents[-2][1][1]) + '"' + """
TREE3_LABEL """ + '"' + tree2_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents[-3][1][1]) + '"' + """
ANTECEDENT_CARRIED """ + antecedent_carried + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal"].add(
actr.chunkstring(string=string))
break
postulated_gaps, reduced_unary = 0, 0
postulate_gaps, reduce_unary = True, True
antecedent_carried = antecedent_carried_origo
# the activation for the first word comes only from the blind
if word_parsed == 0:
activations_list.append(np.mean(activations) / 10)
agreeing_actions_list.append(np.mean(agreeing_actions))
matching_fs_list.append(np.mean(matching_fs))
total_fan_list.append(np.mean(total_fan))
critical.pop(0)
#this loop for potential reanalysis
while True:
if critical[0] != "no":
try:
critical_rule = critical_rules[critical[0]].pop(0)
except KeyError:
critical_rule = None
parser_retrievals, number_of_agreeing_actions, number_of_matching_fs, fan_size = ut.recall_action(
actions,
parser.goals["imaginal_reanalysis"],
parser.goals["word_info"],
critical_rule,
recently_retrieved,
built_constituents_reanalysis,
word_freq,
label_freq,
prints=False,
strength_of_association=strength_of_association,
number_retrieved=3,
postulate_gaps=postulate_gaps,
reduce_unary=reduce_unary,
blind={})
else:
parser_retrievals, number_of_agreeing_actions, number_of_matching_fs, fan_size = ut.recall_action(
actions,
parser.goals["imaginal_reanalysis"],
parser.goals["word_info"],
None,
recently_retrieved,
built_constituents_reanalysis,
word_freq,
label_freq,
prints=False,
strength_of_association=strength_of_association,
number_retrieved=3,
postulate_gaps=postulate_gaps,
reduce_unary=reduce_unary,
blind={})
activations.append(parser_retrievals[0])
agreeing_actions.append(number_of_agreeing_actions)
matching_fs.append(number_of_matching_fs)
total_fan.append(fan_size)
if first_action:
actions_list.append(str(parser_retrievals[1]["action"]))
first_action = False
ut.collect_parse(parser_retrievals[1],
built_constituents_reanalysis)
tree0_label = built_constituents_reanalysis[-1][0].label()
tree1_label = built_constituents_reanalysis[-2][0].label()
tree2_label = built_constituents_reanalysis[-3][0].label()
tree3_label = built_constituents_reanalysis[-4][0].label()
children = {
"".join(["tree", str(x)]): ["NOPOS", "NOPOS"]
for x in range(4)
}
for x, subtree in enumerate(built_constituents[-1][0]):
if isinstance(subtree,
Tree) and subtree.label() != ut.EMPTY:
children["tree0"][x] = subtree.label()
if re.search("_BAR", children["tree0"][1]):
if built_constituents[-1][0][1][1].label(
) == ut.EMPTY or re.search(
"_BAR", built_constituents[-1][0][1][1].label()):
children["tree0"][1] = built_constituents[-1][0][1][
0].label()
else:
children["tree0"][1] = built_constituents[-1][0][1][
1].label()
for x, subtree in enumerate(built_constituents[-2][0]):
if isinstance(subtree,
Tree) and subtree.label() != ut.EMPTY:
children["tree1"][x] = subtree.label()
if re.search("_BAR", children["tree1"][1]):
if built_constituents[-2][0][1][1].label(
) == ut.EMPTY or re.search(
"_BAR", built_constituents[-2][0][1][1].label()):
children["tree1"][1] = built_constituents[-2][0][1][
0].label()
else:
children["tree1"][1] = built_constituents[-2][0][1][
1].label()
if re.search("-TPC", tree0_label) or (re.search(
"^W", tree0_label)):
antecedent_carried = "YES"
what_antecedent_carried = str(tree0_label)
# block looping through reduce_unary (at most 2 reduce_unary allowed)
if parser_retrievals[1] and parser_retrievals[1][
"action"] == 'reduce_unary':
reduced_unary += 1
if reduced_unary == 2:
reduce_unary = False
reduced_unary = 0
else:
reduced_unary = 0
reduce_unary = True
if parser_retrievals[1] and parser_retrievals[1][
"action"] == 'postulate_gap':
if antecedent_carried_origo == "YES" and syntactic and re.search(
"t",
str(parser_retrievals[1]["action_result_label"]
[0])) and retrieve_wh != "yes":
retrieve_wh_reanalysis = "yes" #record that based on the upcoming word info, trace should be postulated; only if the original structure did not postulate it
if re.search(
"t",
str(parser_retrievals[1]["action_result_label"]
[0])):
antecedent_carried = "NO"
#at most 3 gaps allowed
if postulated_gaps > 1:
postulate_gaps = False
postulated_gaps += 1
ci = parser.goals["imaginal_reanalysis"].pop()
parser.decmem.add(ci, time=parser_sim.show_time())
string = """
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(ci.WORD_NEXT0_LEX) + '"' + """
WORD_NEXT0_POS """ + '"' + str(ci.WORD_NEXT0_POS) + '"' + """
ANTECEDENT_CARRIED """ + antecedent_carried + """
TREE0_HEAD """ + '"' + str(
parser_retrievals[1]["action_result_label"][0]
) + '"' + """
TREE0_LABEL '-NONE-'
TREE0_HEADPOS """ + str(built_constituents_reanalysis[-1][1][0]) + """
TREE0_LEFTCHILD """ + children["tree0"][0] + """
TREE0_RIGHTCHILD """ + children["tree0"][1] + """
TREE1_LABEL """ + '"' + tree1_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents_reanalysis[-2][1][0]) + """
TREE1_HEAD """ + '"' + str(
built_constituents_reanalysis[-2][1][1]) + '"' + """
TREE1_LEFTCHILD """ + children["tree1"][0] + """
TREE1_RIGHTCHILD """ + children["tree1"][1] + """
TREE2_LABEL """ + '"' + tree2_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents_reanalysis[-3][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents_reanalysis[-3][1]
[1]) + '"' + """
TREE3_LABEL """ + '"' + tree3_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents_reanalysis[-4][1]
[1]) + '"' + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal_reanalysis"].add(
actr.chunkstring(string=string))
parser.goals["word_info"].add(
actr.chunkstring(string="""
isa word
form '""" + str(parser_retrievals[1]
["action_result_label"][0]) + """'
cat '-NONE-'"""))
elif parser_retrievals[1]:
ci = parser.goals["imaginal_reanalysis"].pop()
parser.decmem.add(ci, time=parser_sim.show_time())
string = """
isa action_chunk
WORD_NEXT0_LEX """ + '"' + str(ci.WORD_NEXT0_LEX) + '"' + """
WORD_NEXT0_POS """ + '"' + str(ci.WORD_NEXT0_POS) + '"' + """
ANTECEDENT_CARRIED """ + antecedent_carried + """
TREE0_LABEL """ + '"' + str(built_constituents_reanalysis[-1][0].label(
)) + '"' + """
TREE0_HEADPOS """ + str(built_constituents_reanalysis[-1][1][0]) + """
TREE0_HEAD """ + '"' + str(
built_constituents_reanalysis[-1][1][1]) + '"' + """
TREE0_LEFTCHILD """ + children["tree0"][0] + """
TREE0_RIGHTCHILD """ + children["tree0"][1] + """
TREE1_LABEL """ + '"' + tree1_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents_reanalysis[-2][1][0]) + """
TREE1_HEAD """ + '"' + str(built_constituents_reanalysis[-2][1]
[1]) + '"' + """
TREE1_LEFTCHILD """ + children["tree1"][0] + """
TREE1_RIGHTCHILD """ + children["tree1"][1] + """
TREE2_LABEL """ + '"' + tree2_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents_reanalysis[-3][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents_reanalysis[-3][1]
[1]) + '"' + """
TREE3_LABEL """ + '"' + tree3_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents_reanalysis[-4][1]
[1]) + '"' + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal_reanalysis"].add(
actr.chunkstring(string=string))
else:
break
if parser_retrievals[1]["action"] == 'shift':
#sometimes the parser would stop at BAR and shift; in reality, this is not possible since BARs are artificial categories
if re.search("_BAR",
built_constituents_reanalysis[-1][0].label()):
built_constituents_reanalysis[-1][0].set_label(
re.split(
"_BAR", built_constituents_reanalysis[-1]
[0].label())[0])
ci = parser.goals["imaginal_reanalysis"].pop()
parser.decmem.add(ci, time=parser_sim.show_time())
#built constituents have head info; if it is not present, use the info from imaginal_reanalysis (stores head info for terminal nodes)
string = """
isa action_chunk
TREE1_LABEL """ + '"' + tree0_label + '"' + """
TREE1_HEADPOS """ + str(built_constituents_reanalysis[-1][1][0]) + """
TREE1_HEAD """ + '"' + str(
built_constituents_reanalysis[-1][1][1]) + '"' + """
TREE1_LEFTCHILD """ + children["tree0"][0] + """
TREE1_RIGHTCHILD """ + children["tree0"][1] + """
TREE2_LABEL """ + '"' + tree1_label + '"' + """
TREE2_HEADPOS """ + str(built_constituents_reanalysis[-2][1][0]) + """
TREE2_HEAD """ + '"' + str(built_constituents_reanalysis[-2][1]
[1]) + '"' + """
TREE3_LABEL """ + '"' + tree2_label + '"' + """
TREE3_HEAD """ + '"' + str(built_constituents_reanalysis[-3][1]
[1]) + '"' + """
ANTECEDENT_CARRIED """ + antecedent_carried + """
ACTION_PREV """ + str(parser_retrievals[1]["action"])
parser.goals["imaginal_reanalysis"].add(
actr.chunkstring(string=string))
break
cg = parser.goals["g"].pop()
parser.goals["g"].add(
actr.chunkstring(string="""
isa reading
position """ + str(cg.position) + """
reanalysis None
retrieve_wh """ + str(retrieve_wh) + """
what_retrieve """ + str(
what_antecedent_carried) #used only for recall of category
+ """
state finished_recall"""))
if built_constituents != built_constituents_reanalysis:
if reanalysis and len(built_constituents) != len(
built_constituents_reanalysis):
#mark that the reanalysis should take place
parser.goals["g"].add(
actr.chunkstring(string="""
isa reading
position """ + str(cg.position) + """
reanalysis yes
retrieve_wh """ + str(retrieve_wh) + """
what_retrieve """ + str(what_antecedent_carried
) #used only for recall of category
+ """
state finished_recall"""))
if word_parsed not in tobe_removed:
reanalysis_list[-1] = "yes"
if prints:
original_tree = Tree(
"X", next(zip(*built_constituents[3:])))
print("DRAWING TREE TO BE REANALYSED")
print("********************************")
original_tree.draw()
built_constituents = built_constituents_reanalysis.copy()
parser.goals["imaginal"].add(
parser.goals["imaginal_reanalysis"].copy().pop())
final_tree = Tree("X", next(zip(*built_constituents[3:])))
if word_parsed not in tobe_removed:
activations_list.append(np.mean(activations) / 10)
wh_gaps_list.append(str(retrieve_wh))
agreeing_actions_list.append(np.mean(agreeing_actions))
matching_fs_list.append(np.mean(matching_fs))
total_fan_list.append(np.mean(total_fan))
words_list.append(sentence[word_parsed])
critical.pop(0)
if prints:
print("DRAWING TREE")
print("********************************")
# print(final_tree)
# final_tree.pretty_print()
final_tree.draw()
return words_list, activations_list[:
-1], wh_gaps_list, reanalysis_list, agreeing_actions_list[:
-1], matching_fs_list[:
-1], total_fan_list[:
-1]
from nltk.tree import Tree
vp = Tree('VP', [Tree('V', ['saw']), Tree('NP', ['him'])])
s = Tree('S', [Tree('NP', ['I']), vp])
print(s)
dp1 = Tree('dp', [Tree('d', ['the']), Tree('np', ['dog'])])
dp2 = Tree('dp', [Tree('d', ['the']), Tree('np', ['cat'])])
vp = Tree('vp', [Tree('v', ['chased']), dp2])
dp1.draw()
dp2.draw()
vp.draw()
tree = Tree('s', [dp1, vp])
print(tree)
tree.draw()
len(tree)
print(tree.leaves())
tree.label()
dp1.label()
def stanford_parser(dep_parser, sub_definition):
Tree = list(dep_parser.raw_parse(sub_definition))[0]
print Tree
Tree.draw()
def draw_tree(tree):
''' Draws a tree... '''
Tree.draw(tree)
the_tree = Tree("sentence", tokenize.sent_tokenize(initial_sentence))
#the_tree.draw()
print(the_tree)
list_of_sentences = []
for i in tokenize.sent_tokenize(initial_sentence):
list_of_sentences.append(tag.pos_tag(tokenize.word_tokenize(i)))
print(list_of_sentences)
x = Tree("ID:50", list_of_sentences)
print(x)
x.draw()
'''
for index, value in enumerate(the_tree):
print("index" + str(index))
print("value" + str(value))
print(tokenize.word_tokenize(the_tree[index]))
the_tree = Tree(tokenize.word_tokenize(the_tree[index]))
the_tree.draw()
'''
'''
print(tokenize.sent_tokenize(initial_sentence))
print("FOR LOOP")
for i in tokenize.sent_tokenize(initial_sentence):
for child in root:
cur_str = ''
if isinstance(child, Tree):
cur_str = depth_travel(child)
cur_str = child.label() + cur_str
else:
cur_str = '\"' + child + '\"'
result_str += cur_str + "+"
result_str = result_str[:-1] + ")"
return result_str
if __name__ == '__main__':
#添加stanford环境变量,此处需要手动修改，jar包地址为绝对地址。
parser_path = r'D:\Workspace\Eclipse\Stanford-parser\lib\stanford-parser.jar'
parser_model_path = r'D:\Workspace\Eclipse\Stanford-parser\lib\stanford-parser-2016.10.31-models.jar'
parser_lex_model_path = r"D:\Workspace\Eclipse\Stanford-parser\model_en\lexparser\englishPCFG.ser.gz"
#句法标注
# parser = stanford.StanfordParser(model_path=parser_lex_model_path, path_to_jar=parser_path, path_to_models_jar=parser_model_path)
parser = stanford.StanfordParser(model_path=parser_lex_model_path,
path_to_jar=parser_path,
path_to_models_jar=parser_model_path)
tree = Tree(
'ROOT',
list(
next(
parser.raw_parse(
"the quick brown fox jumps over the lazy dog"))))
print(depth_travel(tree))
tree.draw()
def denpendency_stanford_dp(tokenized_sent, display_tree=False):
result = list(stanford_dp.tagged_parse(tokenized_sent))
for item in result[0].triples():
if display_tree:
Tree.draw(item)
print item