def morph_character_parsing(self, text):
"""
Extracts the words from the text, performs morphological analysis on the words, and then parses the text character by character.
"""
lexRules = self.morphword_pairs_to_rules(self.get_all_words(text))
g = grammar.FeatureGrammar.fromstring(self.grammarText + lexRules)
p = parse.FeatureEarleyChartParser(g)
return p.parse(text)
def add_to_grammar(rules):
'''
This function adds in new rules for the grammar.
'''
global GRAMMARSTR
global PARSER
for rule in rules:
GRAMMARSTR += '\n' + rule
GRAMMAR = grammar.FeatureGrammar.fromstring(GRAMMARSTR)
PARSER = parse.FeatureEarleyChartParser(GRAMMAR)
def gramatica(self, texto):
print(texto)
flag = True
x = ""
gramatica2 = grammar.FeatureGrammar.fromstring("""
% start S
S[SEM=<?vp(?np)>] -> VP[SEM=?vp] NP[SEM=?np]
S[SEM=<jugar(?np)>] -> NP[SEM=?np]
VP[SEM=?v] -> V[SEM=?v] PREPO
V[SEM=<\\x.jugar(x)>] -> "tirar" | "jugar" | "poner"
PREPO -> "en"
NP[SEM=?num] -> ARTICULO SUSTANTIVO NUM[SEM=?num]
NP[SEM=?num] -> PREPO ARTICULO NUM[SEM=?num]
NP[SEM=?num] -> ARTICULO NUM[SEM=?num]
ARTICULO -> "el" | "la"
SUSTANTIVO -> "casilla" | "posición" | "número" | "espacio"
NUM[SEM=<1>] -> "1" | "uno"
NUM[SEM=<2>] -> "2" | "dos"
NUM[SEM=<3>] -> "3" | "tres"
NUM[SEM=<4>] -> "4" | "cuatro"
NUM[SEM=<5>] -> "5" | "cinco"
NUM[SEM=<6>] -> "6" | "seis"
NUM[SEM=<7>] -> "7" | "siete"
NUM[SEM=<8>] -> "8" | "ocho"
NUM[SEM=<9>] -> "9" | "nueve"
""")
while (flag):
try:
parser = parse.FeatureEarleyChartParser(gramatica2)
tokens = texto.split()
tree = parser.parse_one(tokens)
semantica = tree.label()['SEM']
x = (str(semantica).split("("))[1].replace(")", "")
flag = False
except:
self.ejecutarVoz(
"No pude reconocer lo que dijiste, intenta de nuevo")
texto = self.reconocerVoz()
self.gramatica(texto)
return x
def __init__(self):
self.sql_grammar = """
% start S
S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp]
VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp]
VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap]
NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n]
PP[SEM=(?p + ?np)] -> P[SEM=?p] NP[SEM=?np]
AP[SEM=?pp] -> A[SEM=?a] PP[SEM=?pp]
NP[SEM='Country="greece"'] -> 'Greece' | 'greece'
NP[SEM='Country="africa"'] -> 'africa'
NP[SEM='Country="thailand"'] -> 'thailand'
NP[SEM='Country="china"'] -> 'China' | 'china'
Det[SEM='SELECT'] -> 'Which' | 'What' | 'which' | 'what'
N[SEM='City FROM city_table'] -> 'cities'
N[SEM='Population FROM city_table'] -> 'population'
IV[SEM=''] -> 'are'
IV[SEM=''] -> 'is'
A[SEM=''] -> 'located'
P[SEM=''] -> 'in'
"""
self.grammar = grammar.FeatureGrammar.fromstring(self.sql_grammar)
self.parser = parse.FeatureEarleyChartParser(self.grammar)
self.menu_display()
import nltk
from nltk import grammar, parse
from nltk.parse.generate import generate
text = open("fbg2.txt", "r")
grammar = grammar.FeatureGrammar.fromstring(text)
tokens = 'Kim sees this cars'.split()
parser = parse.FeatureEarleyChartParser(grammar)
trees = parser.parse(tokens)
for tree in trees:
print(tree)
for sentence in generate(grammar, n=20):
print(' '.join(sentence))
def construct_parser(self):
g = self.construct_feature_grammar()
self.parser = parse.FeatureEarleyChartParser(g)
def program(percept):
game, nplayer = percept
grammar1 = grammar.FeatureGrammar.fromstring("""
%start S
S[SEM=<vp(?np)>] -> VP[SEM=?np] NP[SEM=?vp]
VP[SEM=<jugar>] -> "Jugar" | "jugar"
NP[SEM=?v] -> CO[SEM=?v]
CO[SEM=<\\x.coord(x)>] -> "1,1" | "1,2" | "1,3" | "2,1" | "2,2" | "2,3" | "3,1" | "3,2" | "3,3"
""")
if nplayer == 1:
while True:
r = sr.Recognizer()
with sr.Microphone() as source:
print("Di tu tirada con 'Jugar x,y'...")
r.adjust_for_ambient_noise(source, duration=1)
audio = r.listen(source,phrase_time_limit=5)
estado = r.recognize_google(audio, language="es-MX")
print(estado)
tokens = estado.split()
rd_parser = parse.FeatureEarleyChartParser(grammar1)
error=0
try:
for tree in rd_parser.parse(tokens):
error=""
except ValueError:
error="Error, gramática no válida"
print("Audio detectado: "+estado)
if error=="":
try:
if estado == "jugar 1,1":
move = '1'
estado = '1'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 1,2":
move = '2'
estado = '2'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 1,3":
move = '3'
estado = '3'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 2,1":
move = '4'
estado = '4'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 2,2":
move = '5'
estado = '5'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 2,3":
move = '6'
estado = '6'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 3,1":
move = '7'
estado = '7'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 3,2":
move = '8'
estado = '8'
speak("El jugador tiró en la casilla"+estado)
elif estado == "jugar 3,3":
move = '9'
estado = '9'
speak("El jugador tiró en la casilla"+estado)
else:
move = '0'
estado = '0'
speak("No es una jugada válida")
return estado
except sr.UnknownValueError:
print("No se reconoce el audio")
except sr.RequestError as e:
print("No puede generar resultados del servicio de Google Speech Recognition; {0}".format(e))
else:
print("El audio hablado no es válido en la gramática.")
if estado in ('1','2','3','4','5','6','7','8','9'):
move = estado
else:
print(estado+" no es válido, vuelve a decir tu jugada: ")
r.adjust_for_ambient_noise(source, duration=1)
audio = r.listen(source,phrase_time_limit=5)
estado = r.recognize_google(audio, language="es-MX")
move = estado
return move
break
if nplayer == 2:
if algorithm(game):
move = algorithm(game)
return move
JJ[SEM='Section="SHIFT DIFFERENTIAL"'] -> 'Shift_Differential'
JJ[SEM='Section="SALARIES"'] -> 'Salaries'
JJ[SEM='Section="SIGN LANGUAGE PREMIUM"'] -> 'Sign_Language'
JJ[SEM='Section="COURT APPEARANCES"'] -> 'Court_Appearances'
JJ[SEM='Section="CIVIC DUTY"'] -> 'Civic_Duty'
JJ[SEM='Section="JURY SERVICE"'] -> 'Jury_Service'
JJ[SEM='Section="MILITARY LEAVE"'] -> 'Military_Leave'
JJ[SEM='Section="CALL BACK PAY"'] -> 'Call_Back'
JJ[SEM='Section="DISTURBANCE CALLS"'] -> 'Disturbance_Calls'
JJ[SEM='Section="ON-CALL/STANDBY COMPENSATION"'] -> 'On-Call/Standby'
JJ[SEM='Section="TEMPORARY SUPERVISORY PAY"'] -> 'Temporary_Supervisory'
JJ[SEM='Section="CIVILIAN SUPERVISORY DIFFERENTIAL"'] -> 'Civilian_Supervisory'
JJ[SEM='Section="MILEAGE"'] -> 'Mileage'
"""
SQL_grammar = grammar.FeatureGrammar.fromstring(SQL_rule)
SQL_parser = parse.FeatureEarleyChartParser(SQL_grammar)
# simple application to allow user search bonus info based on conditions
def app():
condition = ''
section_names = ['Salaries','Overtime','Shift_Differential','Bilingual','Sign_Language','Court_Appearances','Civic_Duty','Jury_Service','Military_Leave','Call_Back','Disturbance_Calls','On-Call/Standby','Mileage','Temporary_Supervisory']
while condition not in section_names:
condition = str(input('\n Salaries, Overtime, Shift_Differential, Bilingual, Sign_Language, Court_Appearances, Civic_Duty, Jury_Service, Military_Leave, Call_Back, Disturbance_Calls, On-Call/Standby, Mileage, Temporary_Supervisory, Civilian_Supervisory.\nPlease type in a correct section you want to search for bonus info: '''))
question = 'Which employees will receive what percentage/amount of bonus under ' + str(condition) + ' condition'
# translate input question into SQL query with grammar structure
query_tree = SQL_parser.parse(question.split())
for tree in query_tree:
root = tree.label()['SEM']
SQL_query = list(root)
query = ' '.join(str(w) for w in SQL_query if w)
S[SEM=<?vp(?np)>] -> VP[SEM=?vp] NP[SEM=?np] S[SEM=<jugar(?np)>] -> NP[SEM=?np] VP[SEM=?v] -> V[SEM=?v] PREPO V[SEM=<\\x.jugar(x)>] -> "tirar" | "jugar" | "poner" PREPO -> "en" NP[SEM=?num] -> ARTICULO SUSTANTIVO NUM[SEM=?num] NP[SEM=?num] -> PREPO ARTICULO NUM[SEM=?num] NP[SEM=?num] -> ARTICULO NUM[SEM=?num] ARTICULO -> "el" | "la" SUSTANTIVO -> "casilla" | "posición" | "número" | "espacio" NUM[SEM=<1>] -> "1" | "uno" NUM[SEM=<2>] -> "2" | "dos" NUM[SEM=<3>] -> "3" | "tres" NUM[SEM=<4>] -> "4" | "cuatro" NUM[SEM=<5>] -> "5" | "cinco" NUM[SEM=<6>] -> "6" | "seis" NUM[SEM=<7>] -> "7" | "siete" NUM[SEM=<8>] -> "8" | "ocho" NUM[SEM=<9>] -> "9" | "nueve" """) parser = parse.FeatureEarleyChartParser(gramatica2) text = "jugar en la casilla 9" tokens = text.split() trees = parser.parse(tokens) semantica = tree.label()['SEM']
DT[SEM=<a>] -> 'a'
DT[SEM=<a>] -> 'an'
IN[SEM=<with>] -> 'with'
IN[SEM=<for>] -> 'for'
IN[SEM=<about>] -> 'about'
TO[SEM=<to>]-> 'to'
DT[SEM=<the>] -> 'the'
IN[SEM=<into>] -> 'into'
CC[SEM=<\\x.(\\y.(x & y))>] -> 'and'
"""
#stuff that doesn't work yet
"""
NP[N=<?jj(?nn)>] -> JJ[SEM=?jj] NN[SEM=?nn]
"""
GRAMMAR = grammar.FeatureGrammar.fromstring(GRAMMARSTR)
PARSER = parse.FeatureEarleyChartParser(GRAMMAR)
def add_to_grammar(rules):
'''
This function adds in new rules for the grammar.
'''
global GRAMMARSTR
global PARSER
for rule in rules:
GRAMMARSTR += '\n' + rule
GRAMMAR = grammar.FeatureGrammar.fromstring(GRAMMARSTR)
PARSER = parse.FeatureEarleyChartParser(GRAMMAR)
def parse_sentence(sentence):
def upload():
try:
# Read the Sinhala Grammar
singram = grammar.FeatureGrammar.fromstring(sinhala)
parser = parse.FeatureEarleyChartParser(singram)
# strLine = "මිනිස්සු කුරුල්ලන් ලගින ගස කපති"
# strLine = request.form["text"]
strLine = request.json['InputText']
tokens = strLine.split()
trees = parser.parse(tokens)
chkStrgFirst = "Test"
chkStrgFinal = "Test"
sentenceSize = []
print(len(tokens))
if len(tokens) == 2:
i = 0
for tree in trees:
for node in tree:
for nodenode in node:
print(str(node))
if i == 0:
chkStrgFirst = str(nodenode)
print(str(nodenode) + '\n')
i = i + 1
chkStrgFinal = str(nodenode)
if len(tokens) == 3:
i = 0
for tree in trees:
for node in tree:
for nodenode in node:
if i == 0:
chkStrgFirst = str(nodenode)
print(str(nodenode) + '\n')
i = i + 1
chkStrgFinal = str(nodenode)
if len(tokens) == 4:
i = 0
for tree in trees:
for node in tree:
for nodenode in node:
if i == 0:
chkStrgFirst = str(nodenode)
print(str(nodenode) + '\n')
i = i + 1
chkStrgFinal = str(nodenode)
print(chkStrgFirst)
print(chkStrgFinal)
chkStrgFirstArry = chkStrgFirst.split("[")
chkStrgFirst = chkStrgFirstArry[1]
chkStrgFirstArry = chkStrgFirst.split("]")
chkStrgFirst = chkStrgFirstArry[0]
print(chkStrgFirst)
print("++++++++++++++")
chkStrgFinalArry = chkStrgFinal.split("[")
chkStrgFinal = chkStrgFinalArry[1]
chkStrgFinalArry = chkStrgFinal.split("]")
chkStrgFinal = chkStrgFinalArry[0]
print(chkStrgFinal)
jstre = json.dumps(tree)
jsting = json.loads(jstre)
newstr = str(jsting)
newstr02 = newstr.replace("[", "")
newstr03 = newstr02.replace("]", "")
print(newstr03)
tense = "test"
singular_plural = "test"
gramr = "correct grammer"
if "TENSE='Npast'" in chkStrgFinal:
tense = "present"
if "TENSE='past'" in chkStrgFinal:
tense = "past"
if "NUM='pl'" in chkStrgFirst and "NUM='pl'" in chkStrgFinal:
singular_plural = "plural"
if "NUM='sg'" in chkStrgFirst and "NUM='sg'" in chkStrgFinal:
singular_plural = "singular"
if tense == "test" or singular_plural == "test":
gramr = "wrong grammer"
#print(sentenceSize)
jObj = {}
jObj["gramr"] = gramr
jObj["tense"] = tense
jObj["singular_plural"] = singular_plural
except:
tense = "test"
singular_plural = "test"
gramr = "wrong grammer"
jObj = {}
jObj["gramr"] = gramr
jObj["tense"] = tense
jObj["singular_plural"] = singular_plural
return Response(json.dumps(jObj), mimetype='application/json')
#manner
##################
#VLT = volition
#true
#falls
##################
from lang_helper import sinhala
from nltk import grammar, parse
# Read the Sinhala Grammar
singram = grammar.FeatureGrammar.fromstring(sinhala)
parser = parse.FeatureEarleyChartParser(singram)
f_read = open('sentences.txt', encoding='utf-8')
f_write = open('parsedsents.txt', encoding='utf-8', mode='w')
for line in f_read:
tokens = line.split()
trees = parser.parse(tokens)
for tree in trees:
f_write.write(str(tree) + '\n')
print(str(tree) + '\n')
''' Sample Output
(S[]
(NP[CASE='F1', DEF=?TF, GEN='NE', NUM='pl']
