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Commit cdcf6701 by Steven Bird
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Merge branch 'parseri' into test

parents 8dcae605 04f8a9b6
Showing with 213 additions and 171 deletions
ChangeLog
Version 3.0.2 2015-02-08
* make pretty-printing method names consistent
* improvements to Portuguese stemmer
* transition-based dependency parsers
* code clean ups, minor bug fixes
Thanks to the following contributors to 3.0.2:
Long Duong, Saimadhav Heblikar, Helder, Denis Krusko,
Felipe Madrigal, Dmitrijs Milajevs, Nathan Schneider,
0ssifrage, kiwipi.
Version 3.0.1 2015-01-12
* fix setup.py for new version of setuptools
Version 3.0.0 2014-09-07
* minor bugfixes
* added phrase extraction code by Liling Tan and Fredrik Hedman
......
nltk/__init__.py
......@@ -145,7 +145,7 @@ try:
except ImportError:
pass
else:
from nltk import cluster; from .cluster import *
from nltk import cluster
from nltk.downloader import download, download_shell
try:
......
nltk/chunk/util.py
......@@ -548,7 +548,7 @@ def demo():
s = "[ Pierre/NNP Vinken/NNP ] ,/, [ 61/CD years/NNS ] old/JJ ,/, will/MD join/VB [ the/DT board/NN ] ./."
import nltk
t = nltk.chunk.tagstr2tree(s, chunk_label='NP')
print(t.pprint())
t.pprint()
print()
s = """
......@@ -582,7 +582,7 @@ better JJR I-ADJP
"""
conll_tree = conllstr2tree(s, chunk_types=('NP', 'PP'))
print(conll_tree.pprint())
conll_tree.pprint()
# Demonstrate CoNLL output
print("CoNLL output:")
......
nltk/corpus/__init__.py
......@@ -147,6 +147,8 @@ movie_reviews = LazyCorpusLoader(
encoding='ascii')
names = LazyCorpusLoader(
'names', WordListCorpusReader, r'(?!\.).*\.txt', encoding='ascii')
nkjp = LazyCorpusLoader(
'nkjp', NKJPCorpusReader, r'', encoding='utf8')
nps_chat = LazyCorpusLoader(
'nps_chat', NPSChatCorpusReader, r'(?!README|\.).*\.xml', tagset='wsj')
pl196x = LazyCorpusLoader(
......
nltk/corpus/reader/__init__.py
......@@ -93,6 +93,7 @@ from nltk.corpus.reader.framenet import *
from nltk.corpus.reader.udhr import *
from nltk.corpus.reader.bnc import *
from nltk.corpus.reader.sentiwordnet import *
from nltk.corpus.reader.nkjp import *
# Make sure that nltk.corpus.reader.bracket_parse gives the module, not
# the function bracket_parse() defined in nltk.tree:
......@@ -127,5 +128,6 @@ __all__ = [
'CHILDESCorpusReader', 'AlignedCorpusReader',
'TimitTaggedCorpusReader', 'LinThesaurusCorpusReader',
'SemcorCorpusReader', 'FramenetCorpusReader', 'UdhrCorpusReader',
'BNCCorpusReader', 'SentiWordNetCorpusReader', 'SentiSynset'
'BNCCorpusReader', 'SentiWordNetCorpusReader', 'SentiSynset',
'NKJPCorpusReader'
]
nltk/parse/__init__.py
......@@ -76,3 +76,4 @@ from nltk.parse.nonprojectivedependencyparser import (NonprojectiveDependencyPar
ProbabilisticNonprojectiveParser)
from nltk.parse.malt import MaltParser
from nltk.parse.evaluate import DependencyEvaluator
from nltk.parse.transitionparser import TransitionParser
nltk/parse/api.py
......@@ -32,7 +32,7 @@ class ParserI(object):
"""
raise NotImplementedError()
def parse(self, sent):
def parse(self, sent, *args, **kwargs):
"""
:return: An iterator that generates parse trees for the sentence.
When possible this list is sorted from most likely to least likely.
......@@ -42,25 +42,25 @@ class ParserI(object):
:rtype: iter(Tree)
"""
if overridden(self.parse_sents):
return next(self.parse_sents([sent]))
return next(self.parse_sents([sent], *args, **kwargs))
elif overridden(self.parse_one):
return (tree for tree in [self.parse_one(sent)] if tree is not None)
return (tree for tree in [self.parse_one(sent, *args, **kwargs)] if tree is not None)
elif overridden(self.parse_all):
return iter(self.parse_all(sent))
return iter(self.parse_all(sent, *args, **kwargs))
else:
raise NotImplementedError()
def parse_sents(self, sents):
def parse_sents(self, sents, *args, **kwargs):
"""
Apply ``self.parse()`` to each element of ``sents``.
:rtype: iter(iter(Tree))
"""
return (self.parse(sent) for sent in sents)
return (self.parse(sent, *args, **kwargs) for sent in sents)
def parse_all(self, sent):
def parse_all(self, sent, *args, **kwargs):
""":rtype: list(Tree)"""
return list(self.parse(sent))
return list(self.parse(sent, *args, **kwargs))
def parse_one(self, sent):
def parse_one(self, sent, *args, **kwargs):
""":rtype: Tree or None"""
return next(self.parse(sent), None)
return next(self.parse(sent, *args, **kwargs), None)
nltk/parse/chart.py
......@@ -1346,9 +1346,9 @@ class ChartParser(ParserI):
# Return the final chart.
return chart
def parse_all(self, tokens, tree_class=Tree):
def parse(self, tokens, tree_class=Tree):
chart = self.chart_parse(tokens)
return chart.parses(self._grammar.start(), tree_class=tree_class)
return iter(chart.parses(self._grammar.start(), tree_class=tree_class))
class TopDownChartParser(ChartParser):
"""
......@@ -1628,9 +1628,8 @@ def demo(choice=None,
print()
cp = ChartParser(grammar, strategies[strategy][1], trace=trace)
t = time.time()
# parses = cp.parse_all(tokens)
parses = cp.parse_all(tokens)
chart = cp.chart_parse(tokens)
parses = list(chart.parses(grammar.start()))
times[strategies[strategy][0]] = time.time()-t
print("Nr edges in chart:", len(chart.edges()))
if numparses:
......
nltk/parse/dependencygraph.py
......@@ -102,7 +102,7 @@ class DependencyGraph(object):
self.nodes[head_address]['deps'].setdefault(relation,[])
self.nodes[head_address]['deps'][relation].append(mod_address)
#self.nodes[head_address]['deps'].append(mod_address)
def connect_graph(self):
"""
......@@ -113,7 +113,7 @@ class DependencyGraph(object):
for node2 in self.nodes.values():
if node1['address'] != node2['address'] and node2['rel'] != 'TOP':
relation = node2['rel']
node1['deps'].setdefault(relation,[])
node1['deps'].setdefault(relation, [])
node1['deps'][relation].append(node2['address'])
#node1['deps'].append(node2['address'])
......@@ -214,17 +214,21 @@ class DependencyGraph(object):
lines = (l.rstrip() for l in input_)
lines = (l for l in lines if l)
cell_number = None
for index, line in enumerate(lines, start=1):
cells = line.split(cell_separator)
nrCells = len(cells)
if cell_number is None:
cell_number = len(cells)
else:
assert cell_number == len(cells)
if cell_extractor is None:
try:
cell_extractor = extractors[nrCells]
cell_extractor = extractors[cell_number]
except KeyError:
raise ValueError(
'Number of tab-delimited fields ({0}) not supported by '
'CoNLL(10) or Malt-Tab(4) format'.format(nrCells)
'CoNLL(10) or Malt-Tab(4) format'.format(cell_number)
)
word, lemma, ctag, tag, feats, head, rel = cell_extractor(cells)
......@@ -246,6 +250,9 @@ class DependencyGraph(object):
}
)
# Make sure that he fake root node has labeled dependencies.
if (cell_number == 3) and (head == 0):
rel = 'ROOT'
self.nodes[head]['deps'][rel].append(index)
if not self.nodes[0]['deps']['ROOT']:
......@@ -271,7 +278,7 @@ class DependencyGraph(object):
"""
node = self.get_by_address(i)
word = node['word']
deps = list(chain.from_iterable(node['deps'].values()))
deps = sorted(chain.from_iterable(node['deps'].values()))
if deps:
return Tree(word, [self._tree(dep) for dep in deps])
......@@ -286,7 +293,7 @@ class DependencyGraph(object):
node = self.root
word = node['word']
deps = chain.from_iterable(node['deps'].values())
deps = sorted(chain.from_iterable(node['deps'].values()))
return Tree(word, [self._tree(dep) for dep in deps])
def triples(self, node=None):
......@@ -299,7 +306,7 @@ class DependencyGraph(object):
node = self.root
head = (node['word'], node['ctag'])
for i in node['deps']:
for i in sorted(chain.from_iterable(node['deps'].values())):
dep = self.get_by_address(i)
yield (head, dep['rel'], (dep['word'], dep['ctag']))
for triple in self.triples(node=dep):
......@@ -458,7 +465,7 @@ Nov. NNP 9 VMOD
. . 9 VMOD
""")
tree = dg.tree()
print(tree.pprint())
tree.pprint()
if nx:
# currently doesn't work
import networkx as NX
......@@ -483,7 +490,7 @@ def conll_demo():
"""
dg = DependencyGraph(conll_data1)
tree = dg.tree()
print(tree.pprint())
tree.pprint()
print(dg)
print(dg.to_conll(4))
......@@ -494,7 +501,8 @@ def conll_file_demo():
for entry in conll_data2.split('\n\n') if entry]
for graph in graphs:
tree = graph.tree()
print('\n' + tree.pprint())
print('\n')
tree.pprint()
def cycle_finding_demo():
......
nltk/parse/malt.py
......@@ -87,56 +87,19 @@ class MaltParser(ParserI):
url='http://www.maltparser.org/',
verbose=verbose)
def parse_all(self, sentence, verbose=False):
"""
Use MaltParser to parse a sentence. Takes a sentence as a list of
words; it will be automatically tagged with this MaltParser instance's
tagger.
:param sentence: Input sentence to parse
:type sentence: list(str)
:return: list(DependencyGraph)
"""
return self.parse_sents([sentence], verbose)
def parse_sents(self, sentences, verbose=False):
"""
Use MaltParser to parse multiple sentence. Takes multiple sentences as a
Use MaltParser to parse multiple sentences. Takes multiple sentences as a
list where each sentence is a list of words.
Each sentence will be automatically tagged with this MaltParser instance's
tagger.
:param sentences: Input sentences to parse
:type sentence: list(list(str))
:return: list(DependencyGraph)
:return: iter(DependencyGraph)
"""
tagged_sentences = [self.tagger.tag(sentence) for sentence in sentences]
return self.tagged_parse_sents(tagged_sentences, verbose)
def parse(self, sentence, verbose=False):
"""
Use MaltParser to parse a sentence. Takes a sentence as a list of words.
The sentence will be automatically tagged with this MaltParser instance's
tagger.
:param sentence: Input sentence to parse
:type sentence: list(str)
:return: ``DependencyGraph`` the dependency graph representation of the sentence
"""
return self.parse_sents([sentence], verbose)[0]
def raw_parse(self, sentence, verbose=False):
"""
Use MaltParser to parse a sentence. Takes a sentence as a string;
before parsing, it will be automatically tokenized and tagged with this
MaltParser instance's tagger.
:param sentence: Input sentence to parse
:type sentence: str
:return: list(DependencyGraph)
"""
words = word_tokenize(sentence)
return self.parse(words, verbose)
return iter(self.tagged_parse_sents(tagged_sentences, verbose))
def tagged_parse(self, sentence, verbose=False):
"""
......@@ -158,7 +121,7 @@ class MaltParser(ParserI):
:param sentences: Input sentences to parse
:type sentence: list(list(tuple(str, str)))
:return: list(``DependencyGraph``) the dependency graph representation
:return: iter(iter(``DependencyGraph``)) the dependency graph representation
of each sentence
"""
......@@ -193,7 +156,7 @@ class MaltParser(ParserI):
raise Exception("MaltParser parsing (%s) failed with exit "
"code %d" % (' '.join(cmd), ret))
return DependencyGraph.load(output_file.name)
return iter(DependencyGraph.load(output_file.name))
finally:
input_file.close()
os.remove(input_file.name)
......@@ -276,8 +239,8 @@ def demo():
maltParser = MaltParser()
maltParser.train([dg1,dg2], verbose=verbose)
print(maltParser.raw_parse('John sees Mary', verbose=verbose).tree().pprint())
print(maltParser.raw_parse('a man runs', verbose=verbose).tree().pprint())
maltParser.parse_one(['John','sees','Mary'], verbose=verbose).tree().pprint()
maltParser.parse_one(['a','man','runs'], verbose=verbose).tree().pprint()
if __name__ == '__main__':
demo()
nltk/parse/nonprojectivedependencyparser.py
......@@ -462,8 +462,8 @@ class ProbabilisticNonprojectiveParser(object):
}
)
#print (g_graph.nodes)
# Fully connect non-root nodes in g_graph
g_graph.connect_graph()
original_graph = DependencyGraph()
......@@ -567,8 +567,10 @@ class ProbabilisticNonprojectiveParser(object):
logger.debug('Betas: %s', betas)
for node in original_graph.nodes.values():
# deps must be a dictionary
#node['deps'] = []
# TODO: It's dangerous to assume that deps it a dictionary
# because it's a default dictionary. Ideally, here we should not
# be concerned how dependencies are stored inside of a dependency
# graph.
node['deps'] = {}
for i in range(1, len(tokens) + 1):
original_graph.add_arc(betas[i][0], betas[i][1])
......@@ -701,22 +703,32 @@ class NonprojectiveDependencyParser(object):
# Filter parses
# ensure 1 root, every thing has 1 head
for analysis in analyses:
root_count = 0
root = []
for i, cell in enumerate(analysis):
if cell == -1:
root_count += 1
root = i
if root_count == 1:
graph = DependencyGraph()
graph.nodes[0]['deps'] = root + 1
for i in range(len(tokens)):
node = {'word': tokens[i], 'address': i+1}
node['deps'] = [j+1 for j in range(len(tokens)) if analysis[j] == i]
graph.nodes[i + 1] = node
# cycle = graph.contains_cycle()
# if not cycle:
yield graph
if analysis.count(-1) > 1:
# there are several root elements!
continue
graph = DependencyGraph()
graph.root = graph.nodes[analysis.index(-1) + 1]
for address, (token, head_index) in enumerate(zip(tokens, analysis), start=1):
head_address = head_index + 1
node = graph.nodes[address]
node.update(
{
'word': token,
'address': address,
}
)
if head_address == 0:
rel = 'ROOT'
else:
rel = ''
graph.nodes[head_index + 1]['deps'][rel].append(address)
# TODO: check for cycles
yield graph
#################################################################
......
nltk/parse/stanford.py
......@@ -109,18 +109,6 @@ class StanfordParser(ParserI):
cur_lines.append(line)
return res
def parse_all(self, sentence, verbose=False):
"""
Use StanfordParser to parse a sentence. Takes a sentence as a list of
words; it will be automatically tagged with this StanfordParser instance's
tagger.
:param sentence: Input sentence to parse
:type sentence: list(str)
:rtype: Tree
"""
return self.parse_sents([sentence], verbose)
def parse_sents(self, sentences, verbose=False):
"""
Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
......
nltk/parse/transitionparser.py
......@@ -8,16 +8,16 @@
import tempfile
import pickle
import os
import copy
import operator
from nltk.parse.api import ParserI
import scipy.sparse as sparse
import numpy as np
from os import remove
from copy import deepcopy
from operator import itemgetter
from scipy import sparse
from numpy import array
from sklearn.datasets import load_svmlight_file
from sklearn import svm
from nltk.parse import DependencyGraph
from evaluate import DependencyEvaluator
from nltk.parse import ParserI, DependencyGraph, DependencyEvaluator
class Configuration(object):
......@@ -522,7 +522,7 @@ class TransitionParser(ParserI):
# Save the model to file name (as pickle)
pickle.dump(model, open(modelfile, 'wb'))
finally:
os.remove(input_file.name)
remove(input_file.name)
def parse(self, depgraphs, modelFile):
"""
......@@ -549,9 +549,9 @@ class TransitionParser(ParserI):
col.append(self._dictionary[feature])
row.append(0)
data.append(1.0)
np_col = np.array(sorted(col)) # NB : index must be sorted
np_row = np.array(row)
np_data = np.array(data)
np_col = array(sorted(col)) # NB : index must be sorted
np_row = array(row)
np_data = array(data)
x_test = sparse.csr_matrix((np_data, (np_row, np_col)), shape=(1, len(self._dictionary)))
......@@ -570,7 +570,7 @@ class TransitionParser(ParserI):
# votes[j] +=1
# k +=1
# Sort votes according to the values
#sorted_votes = sorted(votes.items(), key=operator.itemgetter(1), reverse=True)
#sorted_votes = sorted(votes.items(), key=itemgetter(1), reverse=True)
# We will use predict_proba instead of decision_function
prob_dict = {}
......@@ -579,7 +579,7 @@ class TransitionParser(ParserI):
prob_dict[i] = pred_prob[i]
sorted_Prob = sorted(
prob_dict.items(),
key=operator.itemgetter(1),
key=itemgetter(1),
reverse=True)
# Note that SHIFT is always a valid operation
......@@ -609,7 +609,7 @@ class TransitionParser(ParserI):
# Finish with operations build the dependency graph from Conf.arcs
new_depgraph = copy.deepcopy(depgraph)
new_depgraph = deepcopy(depgraph)
for key in new_depgraph.nodes:
node = new_depgraph.nodes[key]
node['rel'] = ''
......@@ -727,7 +727,7 @@ def demo():
Number of training examples : 1
Number of valid (projective) examples : 1
...
>>> os.remove(input_file.name)
>>> remove(input_file.name)
B. Check the ARC-EAGER training
......@@ -743,7 +743,7 @@ def demo():
Number of valid (projective) examples : 1
...
>>> os.remove(input_file.name)
>>> remove(input_file.name)
###################### Check The Parsing Function ########################
......
nltk/test/dependency.doctest
......@@ -35,30 +35,33 @@ CoNLL Data
... . . 9 VMOD
... """
>>> dg = DependencyGraph(treebank_data)
>>> print(dg.tree().pprint())
>>> dg.tree().pprint()
(will
(Vinken Pierre , (old (years 61)) ,)
(join (board the) (as (director a nonexecutive)) (Nov. 29) .))
>>> print(list(dg.triples()))
[((u'will', u'MD'), u'SUB', (u'Vinken', u'NNP')),
((u'Vinken', u'NNP'), u'NMOD', (u'Pierre', u'NNP')),
((u'Vinken', u'NNP'), u'P', (u',', u',')),
((u'Vinken', u'NNP'), u'NMOD', (u'old', u'JJ')),
((u'old', u'JJ'), u'AMOD', (u'years', u'NNS')),
((u'years', u'NNS'), u'NMOD', (u'61', u'CD')),
((u'Vinken', u'NNP'), u'P', (u',', u',')),
((u'will', u'MD'), u'VC', (u'join', u'VB')),
((u'join', u'VB'), u'OBJ', (u'board', u'NN')),
((u'board', u'NN'), u'NMOD', (u'the', u'DT')),
((u'join', u'VB'), u'VMOD', (u'as', u'IN')),
((u'as', u'IN'), u'PMOD', (u'director', u'NN')),
((u'director', u'NN'), u'NMOD', (u'a', u'DT')),
((u'director', u'NN'), u'NMOD', (u'nonexecutive', u'JJ')),
((u'join', u'VB'), u'VMOD', (u'Nov.', u'NNP')),
((u'Nov.', u'NNP'), u'NMOD', (u'29', u'CD')),
((u'join', u'VB'), u'VMOD', (u'.', u'.'))]
>>> for head, rel, dep in dg.triples():
... print(
... '({h[0]}, {h[1]}), {r}, ({d[0]}, {d[1]})'
... .format(h=head, r=rel, d=dep)
... )
(will, MD), SUB, (Vinken, NNP)
(Vinken, NNP), NMOD, (Pierre, NNP)
(Vinken, NNP), P, (,, ,)
(Vinken, NNP), NMOD, (old, JJ)
(old, JJ), AMOD, (years, NNS)
(years, NNS), NMOD, (61, CD)
(Vinken, NNP), P, (,, ,)
(will, MD), VC, (join, VB)
(join, VB), OBJ, (board, NN)
(board, NN), NMOD, (the, DT)
(join, VB), VMOD, (as, IN)
(as, IN), PMOD, (director, NN)
(director, NN), NMOD, (a, DT)
(director, NN), NMOD, (nonexecutive, JJ)
(join, VB), VMOD, (Nov., NNP)
(Nov., NNP), NMOD, (29, CD)
(join, VB), VMOD, (., .)
Using the dependency-parsed version of the Penn Treebank corpus sample.
......@@ -159,21 +162,22 @@ Non-Projective Dependency Parsing
'dog' -> 'his'
>>> dp = NonprojectiveDependencyParser(grammar)
>>> for g in dp.parse(['the', 'man', 'taught', 'his', 'dog', 'to', 'play', 'golf']):
... print(g) # doctest: +NORMALIZE_WHITESPACE
{0: {'address': 0,
'ctag': 'TOP',
'deps': 3,
'feats': None,
'lemma': None,
'rel': 'TOP',
'tag': 'TOP',
'word': None},
1: {'address': 1, 'deps': [], 'word': 'the'},
2: {'address': 2, 'deps': [1], 'word': 'man'},
3: {'address': 3, 'deps': [2, 7], 'word': 'taught'},
4: {'address': 4, 'deps': [], 'word': 'his'},
5: {'address': 5, 'deps': [4], 'word': 'dog'},
6: {'address': 6, 'deps': [], 'word': 'to'},
7: {'address': 7, 'deps': [5, 6, 8], 'word': 'play'},
8: {'address': 8, 'deps': [], 'word': 'golf'}}
>>> g, = dp.parse(['the', 'man', 'taught', 'his', 'dog', 'to', 'play', 'golf'])
>>> print(g.root['word'])
taught
>>> for _, node in sorted(g.nodes.items()):
... if node['word'] is not None:
... print('{address} {word}: {d}'.format(d=node['deps'][''], **node))
1 the: []
2 man: [1]
3 taught: [2, 7]
4 his: []
5 dog: [4]
6 to: []
7 play: [5, 6, 8]
8 golf: []
>>> print(g.tree())
(taught (man the) (play (dog his) to golf))
nltk/test/gluesemantics.doctest
......@@ -370,7 +370,7 @@ Dependency Graph to LFG f-structure
subj g:[pred 'John']]
>>> fstruct.to_depgraph().tree().pprint()
'(sees (dog a) John)'
(sees (dog a) John)
---------------------------------
LFG f-structure to Glue
......
nltk/test/tree.doctest
......@@ -41,7 +41,7 @@ tree object to one of several standard tree encodings:
There is also a fancy ASCII art representation:
>>> tree.pprint()
>>> tree.pretty_print()
s
________|_____
| vp
......@@ -52,7 +52,7 @@ There is also a fancy ASCII art representation:
| | | | |
the dog chased the cat
>>> tree.pprint(unicodelines=True, nodedist=4)
>>> tree.pretty_print(unicodelines=True, nodedist=4)
s
┌──────────────┴────────┐
│ vp
......
nltk/tree.py
......@@ -685,7 +685,7 @@ class Tree(list):
from nltk.draw.tree import draw_trees
draw_trees(self)
def pprint(self, sentence=None, highlight=(), **viz_args):
def pretty_print(self, sentence=None, highlight=(), **viz_args):
"""
Pretty-print this tree as ASCII or Unicode art.
For explanation of the arguments, see the documentation for
......@@ -734,6 +734,17 @@ class Tree(list):
def __str__(self):
return self.pformat()
def pprint(self, **args):
"""
Print a string representation of this Tree to 'stream'
"""
if "stream" in args:
stream = args["stream"]
else:
stream = None
print(self.pformat(**args), file=stream)
def pformat(self, margin=70, indent=0, nodesep='', parens='()', quotes=False):
"""
:return: A pretty-printed string representation of this tree.
......@@ -751,7 +762,7 @@ class Tree(list):
# Try writing it on one line.
s = self._pformat_flat(nodesep, parens, quotes)
if len(s)+indent < margin:
if len(s) + indent < margin:
return s
# If it doesn't fit on one line, then write it on multi-lines.
......
tox.ini
[tox]
envlist = py26,py27,py32,py33,pypy,py26-nodeps,py27-nodeps,py32-nodeps,py33-nodeps,py26-jenkins,py32-jenkins
envlist = py26,py27,py32,py33,py34,pypy,py26-nodeps,py27-nodeps,py32-nodeps,py33-nodeps,py34-nodeps,py26-jenkins,py32-jenkins,py34-jenkins
[testenv]
......@@ -63,6 +63,20 @@ commands =
; python runtests.py --with-coverage --cover-inclusive --cover-package=nltk --cover-html --cover-html-dir={envdir}/docs []
python runtests.py []
[testenv:py34]
deps =
numpy
nose >= 1.2.1
coverage
text-unidecode
commands =
; scipy and scikit-learn requires numpy even to run setup.py so
; they can't be installed in one command
pip install --download-cache={toxworkdir}/_download scipy scikit-learn
; python runtests.py --with-coverage --cover-inclusive --cover-package=nltk --cover-html --cover-html-dir={envdir}/docs []
python runtests.py []
[testenv:py26-nodeps]
basepython = python2.6
......@@ -84,6 +98,11 @@ basepython = python3.3
deps = nose >= 1.2.1
commands = python runtests.py []
[testenv:py34-nodeps]
basepython = python3.4
deps = nose >= 1.2.1
commands = python runtests.py []
[testenv:py26-jenkins]
basepython = python2.6
commands = {toxinidir}/jenkins.sh
......@@ -99,3 +118,11 @@ setenv =
STANFORD_MODELS = {homedir}/third/stanford-parser/
STANFORD_PARSER = {homedir}/third/stanford-parser/
STANFORD_POSTAGGER = {homedir}/third/stanford-postagger/
[testenv:py34-jenkins]
basepython = python3.4
commands = {toxinidir}/jenkins.sh
setenv =
STANFORD_MODELS = {homedir}/third/stanford-parser/
STANFORD_PARSER = {homedir}/third/stanford-parser/
STANFORD_POSTAGGER = {homedir}/third/stanford-postagger/
web/conf.py
......@@ -44,7 +44,7 @@ master_doc = 'index'
# General information about the project.
project = 'NLTK'
copyright = '2013, NLTK Project'
copyright = '2015, NLTK Project'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
......
web/contribute.rst
......@@ -5,14 +5,14 @@ The Natural Language Toolkit exists thanks to the efforts of dozens
of voluntary developers who have contributed functionality and
bugfixes since the project began in 2000 (`contributors <https://github.com/nltk/nltk#contributing>`_).
In 2014 we are especially keen to improve NLTK coverage for:
In 2015 we are especially keen to improve NLTK coverage for:
`dependency parsing <https://github.com/nltk/nltk/wiki/Dependency-Parsing>`_,
`machine translation <https://github.com/nltk/nltk/wiki/Machine-Translation>`_,
`sentiment analysis <https://github.com/nltk/nltk/wiki/Sentiment-Analysis>`_,
`twitter processing <https://github.com/nltk/nltk/wiki/Twitter-Processing>`_.
New material in these areas will be covered in the second edition of
the NLTK book, anticipated in 2015.
the NLTK book, anticipated in early 2016.
* `desired enhancements <https://github.com/nltk/nltk/issues?labels=enhancement&page=1&state=open>`_
* `contribute a corpus <https://github.com/nltk/nltk/wiki/Adding-a-Corpus>`_
......@@ -29,7 +29,6 @@ Individual packages are maintained by the following people:
:Parsing: `Peter Ljunglöf <http://www.cse.chalmers.se/~peb/>`_, Gothenburg, Sweden (``nltk.parse, nltk.featstruct``)
:Metrics: `Joel Nothman <http://joelnothman.com/>`_, Sydney, Australia (``nltk.metrics, nltk.tokenize.punkt``)
:Python 3: `Mikhail Korobov <http://kmike.ru/>`_, Ekaterinburg, Russia
:Integration: `Morten Minde Neergaard <http://8d.no/>`_, Oslo, Norway
:Releases: `Steven Bird <http://estive.net>`_, Melbourne, Australia
......
web/news.rst
NLTK News
=========
2015
----
NLTK 3.0.1 released : January 2015
Minor packaging update.
2014
----
NLTK 3.0.0 released : September 2014
Minor bugfixes. For full details see:
https://github.com/nltk/nltk/blob/develop/ChangeLog
......@@ -26,6 +35,9 @@ NLTK 3.0a4 released : June 2014
https://github.com/nltk/nltk/blob/develop/ChangeLog
http://nltk.org/nltk3-alpha/
2013
----
NLTK Book Updates : October 2013
We are updating the NLTK book for Python 3 and NLTK 3; please see
http://nltk.org/book3/
......
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