def demo():
gfile = GrammarFile.read_file('test.cfg')
cp = gfile.earley_parser()
sent = 'the police read the solutions that Poirot sent'
tokens = list(tokenize.whitespace(sent))
trees = cp.parse_n(tokens)
for tree in trees: print tree
def processWhitespacesWithoutStopWords(self, corpus, caseSensitive):
# initialise token buffer
tokens = []
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# compile regular expression for matching whitespaces
whitespaces = re.compile(r'\s\ \;')
# go through each token in corpus
for token in tokenizedCorpus:
# if case-sensitive handling of tokens
if caseSensitive == 1:
pass
else:
token = token.lower()
# remove white spaces at beginning
token = whitespaces.sub('', token)
# append token to list
tokens.append(token)
# return tokens
return tokens
def processWhitespacesWithoutStopWords(self, corpus, caseSensitive):
# initialise token buffer
tokens = []
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# compile regular expression for matching whitespaces
whitespaces = re.compile(r'\s\ \;')
# go through each token in corpus
for token in tokenizedCorpus:
# if case-sensitive handling of tokens
if caseSensitive == 1:
pass
else:
token = token.lower()
# remove white spaces at beginning
token = whitespaces.sub('', token)
# append token to list
tokens.append(token)
# return tokens
return tokens
def processWhitespaces(self,
corpus,
stopWordList,
caseSensitive,
minimumTokenLength=3,
maximumTokenLength=25):
# initialise token list
tokens = []
# initialise token buffer
tokenBuffer = ''
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# compile regular expression for matching special characters
specialCharacters = re.compile(r'\&.+\;')
# compile regular expression for matching whitespaces
whitespaces = re.compile(r'\s|\ \;')
# compile regular expression for sentence-boundary matching
sentenceBoundary = re.compile(r'[\.\:\!\?\,]')
# go through each token in corpus
for token in tokenizedCorpus:
# get token length
tokenLength = len(token)
# see, if token contains special character
specialCharacterMatches = specialCharacters.findall(token)
# reduce special characters to size one
if specialCharacterMatches:
for match in specialCharacterMatches:
tokenLength -= (len(match) - 1)
# if case-sensitive handling of tokens
if caseSensitive == 1:
pass
else:
token = token.lower()
# remove white spaces at beginning and end
token = whitespaces.sub('', token)
# write token to buffer and remove punctuation
tokenBuffer = sentenceBoundary.sub('', token)
# mark stop words
if tokenLength < minimumTokenLength or tokenLength > maximumTokenLength or tokenBuffer in stopWordList or tokenBuffer.lower(
) in stopWordList:
tokens.append(token + '<STOPWORD>')
else:
tokens.append(token)
# return tokens
return tokens
def text_parse(grammar, sent, trace=2, drawtrees=False, latex=False): parser = grammar.earley_parser(trace=trace) print parser._grammar tokens = list(tokenize.whitespace(sent)) trees = parser.parse_n(tokens) if drawtrees: from treeview import TreeView TreeView(trees) else: for tree in trees: if latex: print tree.latex_qtree() else: print tree
def _demo_stemmer(stemmer):
# Tokenize a sample text.
from nltk_lite import tokenize
text = "John was eating icecream"
tokens = tokenize.whitespace(text)
# Print the results.
print stemmer
for word in tokens:
print "%20s => %s" % (word, stemmer.stem(word))
print
def processWhitespaces(self, corpus, stopWordList, caseSensitive, minimumTokenLength = 3, maximumTokenLength = 25):
# initialise token list
tokens = []
# initialise token buffer
tokenBuffer = ''
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# compile regular expression for matching special characters
specialCharacters = re.compile(r'\&.+\;')
# compile regular expression for matching whitespaces
whitespaces = re.compile(r'\s|\ \;')
# compile regular expression for sentence-boundary matching
sentenceBoundary = re.compile(r'[\.\:\!\?\,]')
# go through each token in corpus
for token in tokenizedCorpus:
# get token length
tokenLength = len(token)
# see, if token contains special character
specialCharacterMatches = specialCharacters.findall(token)
# reduce special characters to size one
if specialCharacterMatches:
for match in specialCharacterMatches:
tokenLength -= (len(match) - 1)
# if case-sensitive handling of tokens
if caseSensitive == 1:
pass
else:
token = token.lower()
# remove white spaces at beginning and end
token = whitespaces.sub('', token)
# write token to buffer and remove punctuation
tokenBuffer = sentenceBoundary.sub('', token)
# mark stop words
if tokenLength < minimumTokenLength or tokenLength > maximumTokenLength or tokenBuffer in stopWordList or tokenBuffer.lower() in stopWordList:
tokens.append(token + '<STOPWORD>')
else:
tokens.append(token)
# return tokens
return tokens
def demo():
import sys, time
S = GrammarCategory.parse('S')
VP = GrammarCategory.parse('VP')
NP = GrammarCategory.parse('NP')
PP = GrammarCategory.parse('PP')
V = GrammarCategory.parse('V')
N = GrammarCategory.parse('N')
P = GrammarCategory.parse('P')
Name = GrammarCategory.parse('Name')
Det = GrammarCategory.parse('Det')
DetSg = GrammarCategory.parse('Det[-pl]')
DetPl = GrammarCategory.parse('Det[+pl]')
NSg = GrammarCategory.parse('N[-pl]')
NPl = GrammarCategory.parse('N[+pl]')
# Define some grammatical productions.
grammatical_productions = [
cfg.Production(S, (NP, VP)), cfg.Production(PP, (P, NP)),
cfg.Production(NP, (NP, PP)),
cfg.Production(VP, (VP, PP)), cfg.Production(VP, (V, NP)),
cfg.Production(VP, (V,)), cfg.Production(NP, (DetPl, NPl)),
cfg.Production(NP, (DetSg, NSg))]
# Define some lexical productions.
lexical_productions = [
cfg.Production(NP, ('John',)), cfg.Production(NP, ('I',)),
cfg.Production(Det, ('the',)), cfg.Production(Det, ('my',)),
cfg.Production(Det, ('a',)),
cfg.Production(NSg, ('dog',)), cfg.Production(NSg, ('cookie',)),
cfg.Production(V, ('ate',)), cfg.Production(V, ('saw',)),
cfg.Production(P, ('with',)), cfg.Production(P, ('under',)),
]
earley_grammar = cfg.Grammar(S, grammatical_productions)
earley_lexicon = {}
for prod in lexical_productions:
earley_lexicon.setdefault(prod.rhs()[0].upper(), []).append(prod.lhs())
sent = 'I saw John with a dog with my cookie'
print "Sentence:\n", sent
from nltk_lite import tokenize
tokens = list(tokenize.whitespace(sent))
t = time.time()
cp = FeatureEarleyChartParse(earley_grammar, earley_lexicon, trace=1)
trees = cp.parse_n(tokens)
print "Time: %s" % (time.time() - t)
for tree in trees: print tree
def raw(files="english-kjv"):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str:
files = (files,)
for file in files:
path = os.path.join(get_basedir(), "genesis", file + ".txt")
s = open(path).read()
for t in tokenize.whitespace(s):
yield t
def getNGramStructure(sourceFile):
# initialise n-gram dictionary
ngrams = {}
# read file
corpus = sourceFile.read()
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# go through each token
for token in tokenizedCorpus:
# split token in single characters
characters = list(token)
# copy character list
charactersBuffer = list(characters)
# initialise buffer
buffer1 = ""
# go through character list
for char1 in characters:
# write each n-gram to list
buffer1 += char1
ngrams[buffer1] = ngrams.get(buffer1, 0) + 1
# shift from character list copy
charactersBuffer.pop(0)
# initialise buffer
buffer2 = ""
# go through copy of character list
for char2 in charactersBuffer:
buffer2 += char2
ngrams[buffer2] = ngrams.get(buffer2, 0) + 1
# return n-grams
return ngrams
def getNGramStructure(sourceFile):
# initialise n-gram dictionary
ngrams = {}
# read file
corpus = sourceFile.read()
# get tokens separated by whitespaces
tokenizedCorpus = tokenize.whitespace(corpus)
# go through each token
for token in tokenizedCorpus:
# split token in single characters
characters = list(token)
# copy character list
charactersBuffer = list(characters)
# initialise buffer
buffer1 = ''
# go through character list
for char1 in characters:
# write each n-gram to list
buffer1 += char1
ngrams[buffer1] = ngrams.get(buffer1, 0) + 1
# shift from character list copy
charactersBuffer.pop(0)
# initialise buffer
buffer2 = ''
# go through copy of character list
for char2 in charactersBuffer:
buffer2 += char2
ngrams[buffer2] = ngrams.get(buffer2, 0) + 1
# return n-grams
return ngrams
def demo():
"""
A demonstration of the recursive descent parser.
"""
from nltk_lite.parse import cfg
# Define some nonterminals
S, VP, NP, PP = cfg.nonterminals('S, VP, NP, PP')
V, N, P, Name, Det = cfg.nonterminals('V, N, P, Name, Det')
# Define a grammar.
productions = (
# Syntactic Productions
cfg.Production(S, [NP, 'saw', NP]),
cfg.Production(S, [NP, VP]),
cfg.Production(NP, [Det, N]),
cfg.Production(VP, [V, NP, PP]),
cfg.Production(NP, [Det, N, PP]),
cfg.Production(PP, [P, NP]),
# Lexical Productions
cfg.Production(NP, ['I']), cfg.Production(Det, ['the']),
cfg.Production(Det, ['a']), cfg.Production(N, ['man']),
cfg.Production(V, ['saw']), cfg.Production(P, ['in']),
cfg.Production(P, ['with']), cfg.Production(N, ['park']),
cfg.Production(N, ['dog']), cfg.Production(N, ['telescope'])
)
grammar = cfg.Grammar(S, productions)
# Tokenize a sample sentence.
sent = list(tokenize.whitespace('I saw a man in the park'))
# Define a list of parsers.
parser = RecursiveDescent(grammar)
parser.trace()
for p in parser.get_parse_list(sent):
print p
def demo():
"""
Create a recursive descent parser demo, using a simple grammar and
text.
"""
from nltk_lite.parse import cfg
grammar = cfg.parse_grammar("""
# Grammatical productions.
S -> NP VP
NP -> Det N PP | Det N
VP -> V NP PP | V NP | V
PP -> P NP
# Lexical productions.
NP -> 'I'
Det -> 'the' | 'a'
N -> 'man' | 'park' | 'dog' | 'telescope'
V -> 'ate' | 'saw'
P -> 'in' | 'under' | 'with'
""")
sent = list(tokenize.whitespace('the dog saw a man in the park'))
RecursiveDescentDemo(grammar, sent).mainloop()
def demo():
"""
Create a shift reduce parser demo, using a simple grammar and
text.
"""
from nltk_lite.parse import cfg
nonterminals = 'S VP NP PP P N Name V Det'
(S, VP, NP, PP, P, N, Name, V, Det) = [cfg.Nonterminal(s)
for s in nonterminals.split()]
productions = (
# Syntactic Productions
cfg.Production(S, [NP, VP]),
cfg.Production(NP, [Det, N]),
cfg.Production(NP, [NP, PP]),
cfg.Production(VP, [VP, PP]),
cfg.Production(VP, [V, NP, PP]),
cfg.Production(VP, [V, NP]),
cfg.Production(PP, [P, NP]),
# Lexical Productions
cfg.Production(NP, ['I']), cfg.Production(Det, ['the']),
cfg.Production(Det, ['a']), cfg.Production(N, ['man']),
cfg.Production(V, ['saw']), cfg.Production(P, ['in']),
cfg.Production(P, ['with']), cfg.Production(N, ['park']),
cfg.Production(N, ['dog']), cfg.Production(N, ['statue']),
cfg.Production(Det, ['my']),
)
grammar = cfg.Grammar(S, productions)
# tokenize the sentence
sent = list(tokenize.whitespace('my dog saw a man in the park with a statue'))
ShiftReduceDemo(grammar, sent).mainloop()
def string2words(s, sep="/"):
return [tag2tuple(t, sep)[0] for t in tokenize.whitespace(s)]
words = []
sentences = []
rowID = 0
# open file
file = open(path + category, 'r')
# add each line to corpus
for line in file:
corpus += line
# close file pointer
file.close()
# get tokens from corpus
tokenizedCorpus = tokenize.whitespace(corpus)
# go through tokens
for token in tokenizedCorpus:
# add token to sentence
words.append(tag.sub('', token))
# if sentence-boundary has been found in this token
if sentenceBoundary.findall(token):
# recompose sentence
for word in words:
sentenceString += word + ' '
# add to sentence string list
sentences.append(sentenceString)
from nltk_lite import tokenize
from nltk_lite.parse import cfg
from nltk_lite.draw.rdparser import RecursiveDescentDemo
productions = """
NP -> NP AND NP
NP -> N
N -> "cabbages"
N -> "kings"
AND -> "and"
"""
grammar = cfg.parse_grammar(productions)
text = list(tokenize.whitespace('cabbages and kings'))
RecursiveDescentDemo(grammar, text).mainloop()
def _chunk_parse(files, chunk_types, top_node, partial_match, collapse_partials, cascade):
# allow any kind of bracketing for flexibility
L_BRACKET = re.compile(r"[\(\[\{<]")
R_BRACKET = re.compile(r"[\)\]\}>]")
if type(files) is str:
files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ycoe/psd", file + ".psd")
s = open(path).read()
data = _parse(s)
for s in data:
bracket = 0
itmType = None
stack = [tree.Tree(top_node, [])]
inTag = []
for itm in list(tokenize.whitespace(s)):
if L_BRACKET.match(itm[0]):
bracket += 1
itm = itm[1:]
matched = False
if partial_match == True:
for eachItm in chunk_types:
if len(eachItm) <= len(itm) and eachItm == itm[: len(eachItm)]:
matched = True
if collapse_partials == True:
itm = eachItm
else:
if chunk_types is not None and itm in chunk_types:
matched = True
if matched == True: # and inTag == 0:
chunk = tree.Tree(itm, [])
if cascade == True:
stack.append(chunk)
inTag += [bracket]
else:
if len(inTag) == 0:
stack[-1].append(chunk)
inTag += [bracket]
itmType = itm
if R_BRACKET.match(itm[-1]):
tmpItm = split(itm, itm[-1])
if tmpItm != "":
if len(inTag) > 0 and inTag[-1] <= bracket: # inTag <= bracket:
if cascade == True:
stack[-1].append((itmType, tmpItm[0]))
else:
stack[-1][-1].append((itmType, tmpItm[0]))
else:
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
stack[-1].append((itmType, tmpItm[0]))
inTag = [] + inTag[:-2]
bracket -= len(tmpItm) - 1
while len(inTag) > 0 and bracket < inTag[-1]:
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
inTag = [] + inTag[:-2]
yield stack
def _list_sent(sent):
return [tokenize.whitespace(line) for line in tokenize.line(sent)]
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
from nltk_lite import tokenize
from nltk_lite.parse import cfg, pcfg, pchart
# Define two demos. Each demo has a sentence and a grammar.
demos = [('I saw John with my cookie', pcfg.toy1),
('the boy saw Jack with Bob under the table with a telescope',
pcfg.toy2)]
# Ask the user which demo they want to use.
print
for i in range(len(demos)):
print '%3s: %s' % (i+1, demos[i][0])
print ' %r' % demos[i][1]
print
print 'Which demo (%d-%d)? ' % (1, len(demos)),
try:
snum = int(sys.stdin.readline().strip())-1
sent, grammar = demos[snum]
except:
print 'Bad sentence number'
return
# Tokenize the sentence.
tokens = list(tokenize.whitespace(sent))
# Define a list of parsers. We'll use all parsers.
parsers = [
pchart.InsideParse(grammar),
pchart.RandomParse(grammar),
pchart.UnsortedParse(grammar),
pchart.LongestParse(grammar),
pchart.BeamParse(len(tokens)+1, grammar)
]
# Run the parsers on the tokenized sentence.
times = []
average_p = []
num_parses = []
all_parses = {}
for parser in parsers:
print '\ns: %s\nparser: %s\ngrammar: %s' % (sent,parser,pcfg)
parser.trace(3)
t = time.time()
parses = parser.get_parse_list(tokens)
times.append(time.time()-t)
if parses: p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
average_p.append(p)
num_parses.append(len(parses))
for p in parses: all_parses[p.freeze()] = 1
# Print some summary statistics
print
print ' Parser | Time (secs) # Parses Average P(parse)'
print '-------------------+------------------------------------------'
for i in range(len(parsers)):
print '%18s |%11.4f%11d%19.14f' % (parsers[i].__class__.__name__,
times[i],num_parses[i],average_p[i])
parses = all_parses.keys()
if parses: p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
print '-------------------+------------------------------------------'
print '%18s |%11s%11d%19.14f' % ('(All Parses)', 'n/a', len(parses), p)
# Ask the user if we should draw the parses.
print
print 'Draw parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
from nltk_lite.draw.tree import draw_trees
print ' please wait...'
draw_trees(*parses)
# Ask the user if we should print the parses.
print
print 'Print parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
for parse in parses:
print parse
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
from nltk_lite import tokenize
from nltk_lite.parse import cfg, pcfg, ViterbiParse
# Define two demos. Each demo has a sentence and a grammar.
demos = [('I saw John with my cookie', pcfg.toy1),
('the boy saw Jack with Bob under the table with a telescope',
pcfg.toy2)]
# Ask the user which demo they want to use.
print
for i in range(len(demos)):
print '%3s: %s' % (i+1, demos[i][0])
print ' %r' % demos[i][1]
print
print 'Which demo (%d-%d)? ' % (1, len(demos)),
try:
snum = int(sys.stdin.readline().strip())-1
sent, grammar = demos[snum]
except:
print 'Bad sentence number'
return
# Tokenize the sentence.
tokens = list(tokenize.whitespace(sent))
parser = ViterbiParse(grammar)
all_parses = {}
print '\nsent: %s\nparser: %s\ngrammar: %s' % (sent,parser,grammar)
parser.trace(3)
t = time.time()
parses = parser.get_parse_list(tokens)
time = time.time()-t
if parses:
average = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else:
average = 0
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print
print 'Time (secs) # Parses Average P(parse)'
print '-----------------------------------------'
print '%11.4f%11d%19.14f' % (time, num_parses, average)
parses = all_parses.keys()
if parses:
p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
print '------------------------------------------'
print '%11s%11d%19.14f' % ('n/a', len(parses), p)
# Ask the user if we should draw the parses.
print
print 'Draw parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
from nltk_lite.draw.tree import draw_trees
print ' please wait...'
draw_trees(*parses)
# Ask the user if we should print the parses.
print
print 'Print parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
for parse in parses:
print parse
def set_sentence(self, sentence):
self._sent = list(tokenize.whitespace(sentence)) #[XX] use tagged?
self.reset()
# initialise co-occurrence matrix
coOccurrences = {}
# open file
file = open(path + category, 'r')
# add each line to corpus
for line in file:
corpus += line
# close file pointer
file.close()
# get tokens from corpus
tokenizedCorpus = tokenize.whitespace(corpus)
# go through tokens
for token in tokenizedCorpus:
# add token to sentence
words.append(tag.sub('', token))
# if sentence-boundary has been found in this token
if sentenceBoundary.findall(token):
# recompose sentence
for word in words:
sentenceString += word + ' '
# add to sentence string list
sentences.append(sentenceString)
