Patterns and Constructions#
from nltk.chunk.regexp import tag_pattern2re_pattern
from nltk.chunk import RegexpParser
from nltk.corpus import brown
import nltk
tag_pattern2re_pattern('<DT>?<NN.*>+')
'(<(DT)>)?(<(NN[^\\{\\}<>]*)>)+'
chunker = RegexpParser('''
NP:
{<DT><NN.*><.*>*<NN.*>}
}<VB.*>{
''')
#sent = brown.tagged_sents()[10]
#chunker.parse(sent)
sent = 'The article looks like a work written by a foreigner.'
sent = nltk.pos_tag(nltk.word_tokenize(sent))
sent_ct = chunker.parse(sent)
print(sent_ct)
(S
(NP The/DT article/NN)
looks/VBZ
(NP like/IN a/DT work/NN)
written/VBN
(NP by/IN a/DT foreigner/NN)
./.)
sent_ct.productions()
#sent_ct.chomsky_normal_form()
sent_ct
sent_ct[0]
The Ghostscript executable isn't found.
See http://web.mit.edu/ghostscript/www/Install.htm
If you're using a Mac, you can try installing
https://docs.brew.sh/Installation then `brew install ghostscript`
---------------------------------------------------------------------------
LookupError Traceback (most recent call last)
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/nltk/tree.py in _repr_png_(self)
805 env_vars=['PATH'],
--> 806 verbose=False,
807 )
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/nltk/internals.py in find_binary(name, path_to_bin, env_vars, searchpath, binary_names, url, verbose)
696 find_binary_iter(
--> 697 name, path_to_bin, env_vars, searchpath, binary_names, url, verbose
698 )
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/nltk/internals.py in find_binary_iter(name, path_to_bin, env_vars, searchpath, binary_names, url, verbose)
680 for file in find_file_iter(
--> 681 path_to_bin or name, env_vars, searchpath, binary_names, url, verbose
682 ):
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/nltk/internals.py in find_file_iter(filename, env_vars, searchpath, file_names, url, verbose, finding_dir)
638 div = '=' * 75
--> 639 raise LookupError('\n\n%s\n%s\n%s' % (div, msg, div))
640
LookupError:
===========================================================================
NLTK was unable to find the gs file!
Use software specific configuration paramaters or set the PATH environment variable.
===========================================================================
During handling of the above exception, another exception occurred:
LookupError Traceback (most recent call last)
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
~/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/nltk/tree.py in _repr_png_(self)
817 "https://docs.brew.sh/Installation then `brew install ghostscript`")
818 print(pre_error_message, file=sys.stderr)
--> 819 raise LookupError
820
821 with open(out_path, 'rb') as sr:
LookupError:
Tree('NP', [('The', 'DT'), ('article', 'NN')])
print(type(sent_ct[0].label))
print(sent_ct[0])
print(type(sent_ct[0].leaves))
print(type(sent_ct[1]))
type(sent_ct[2])
<class 'method'>
(NP The/DT article/NN)
<class 'method'>
<class 'tuple'>
nltk.tree.Tree
i=0
for subtree in sent_ct.subtrees():
i=i+1
print(str(i))
print('label: {}'.format(subtree.label()))
print(subtree)
1
label: S
(S
(NP The/DT article/NN)
looks/VBZ
(NP like/IN a/DT work/NN)
written/VBN
(NP by/IN a/DT foreigner/NN)
./.)
2
label: NP
(NP The/DT article/NN)
3
label: NP
(NP like/IN a/DT work/NN)
4
label: NP
(NP by/IN a/DT foreigner/NN)
str(sent_ct)
'(S\n (NP The/DT article/NN)\n looks/VBZ\n (NP like/IN a/DT work/NN)\n written/VBN\n (NP by/IN a/DT foreigner/NN)\n ./.)'
for subtree in sent_ct.subtrees(filter=lambda t: t.label().endswith("NP")):
print(subtree)
(NP The/DT article/NN)
(NP like/IN a/DT work/NN)
(NP by/IN a/DT foreigner/NN)
# write chunk rules
pat_chunker = RegexpParser('''
ADJ_AND_ADJ:
{<JJ.*><CC><JJ.*>}
''')
for sent in brown.tagged_sents()[:500]:
cur_t = pat_chunker.parse(sent)
cur_pat = [pat for pat in cur_t.subtrees(filter=lambda t: t.label().startswith("ADJ_AND"))]
if len(cur_pat)>0:
print(cur_pat)
[Tree('ADJ_AND_ADJ', [('outmoded', 'JJ'), ('or', 'CC'), ('inadequate', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('fair', 'JJ'), ('and', 'CC'), ('equitable', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('rural', 'JJ'), ('and', 'CC'), ('urban', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('junior', 'JJ'), ('or', 'CC'), ('senior', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('medical', 'JJ'), ('and', 'CC'), ('dental', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('ambitious', 'JJ'), ('and', 'CC'), ('costly', 'JJ')]), Tree('ADJ_AND_ADJ', [('medical', 'JJ'), ('and', 'CC'), ('dental', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('medical', 'JJ'), ('and', 'CC'), ('dental', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('medical', 'JJ'), ('and', 'CC'), ('dental', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('tentative', 'JJ'), ('and', 'CC'), ('exploratory', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('military', 'JJ'), ('and', 'CC'), ('economic', 'JJ')])]
[Tree('ADJ_AND_ADJ', [('firmer', 'JJR'), ('and', 'CC'), ('tougher', 'JJR')])]
[Tree('ADJ_AND_ADJ', [('amateurish', 'JJ'), ('and', 'CC'), ('monumental', 'JJ')])]
nltk.help.upenn_tagset()
$: dollar
$ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$
'': closing quotation mark
' ''
(: opening parenthesis
( [ {
): closing parenthesis
) ] }
,: comma
,
--: dash
--
.: sentence terminator
. ! ?
:: colon or ellipsis
: ; ...
CC: conjunction, coordinating
& 'n and both but either et for less minus neither nor or plus so
therefore times v. versus vs. whether yet
CD: numeral, cardinal
mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-
seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025
fifteen 271,124 dozen quintillion DM2,000 ...
DT: determiner
all an another any both del each either every half la many much nary
neither no some such that the them these this those
EX: existential there
there
FW: foreign word
gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous
lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte
terram fiche oui corporis ...
IN: preposition or conjunction, subordinating
astride among uppon whether out inside pro despite on by throughout
below within for towards near behind atop around if like until below
next into if beside ...
JJ: adjective or numeral, ordinal
third ill-mannered pre-war regrettable oiled calamitous first separable
ectoplasmic battery-powered participatory fourth still-to-be-named
multilingual multi-disciplinary ...
JJR: adjective, comparative
bleaker braver breezier briefer brighter brisker broader bumper busier
calmer cheaper choosier cleaner clearer closer colder commoner costlier
cozier creamier crunchier cuter ...
JJS: adjective, superlative
calmest cheapest choicest classiest cleanest clearest closest commonest
corniest costliest crassest creepiest crudest cutest darkest deadliest
dearest deepest densest dinkiest ...
LS: list item marker
A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005
SP-44007 Second Third Three Two * a b c d first five four one six three
two
MD: modal auxiliary
can cannot could couldn't dare may might must need ought shall should
shouldn't will would
NN: noun, common, singular or mass
common-carrier cabbage knuckle-duster Casino afghan shed thermostat
investment slide humour falloff slick wind hyena override subhumanity
machinist ...
NNP: noun, proper, singular
Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos
Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA
Shannon A.K.C. Meltex Liverpool ...
NNPS: noun, proper, plural
Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists
Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques
Apache Apaches Apocrypha ...
NNS: noun, common, plural
undergraduates scotches bric-a-brac products bodyguards facets coasts
divestitures storehouses designs clubs fragrances averages
subjectivists apprehensions muses factory-jobs ...
PDT: pre-determiner
all both half many quite such sure this
POS: genitive marker
' 's
PRP: pronoun, personal
hers herself him himself hisself it itself me myself one oneself ours
ourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessive
her his mine my our ours their thy your
RB: adverb
occasionally unabatingly maddeningly adventurously professedly
stirringly prominently technologically magisterially predominately
swiftly fiscally pitilessly ...
RBR: adverb, comparative
further gloomier grander graver greater grimmer harder harsher
healthier heavier higher however larger later leaner lengthier less-
perfectly lesser lonelier longer louder lower more ...
RBS: adverb, superlative
best biggest bluntest earliest farthest first furthest hardest
heartiest highest largest least less most nearest second tightest worst
RP: particle
aboard about across along apart around aside at away back before behind
by crop down ever fast for forth from go high i.e. in into just later
low more off on open out over per pie raising start teeth that through
under unto up up-pp upon whole with you
SYM: symbol
% & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***
TO: "to" as preposition or infinitive marker
to
UH: interjection
Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen
huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly
man baby diddle hush sonuvabitch ...
VB: verb, base form
ask assemble assess assign assume atone attention avoid bake balkanize
bank begin behold believe bend benefit bevel beware bless boil bomb
boost brace break bring broil brush build ...
VBD: verb, past tense
dipped pleaded swiped regummed soaked tidied convened halted registered
cushioned exacted snubbed strode aimed adopted belied figgered
speculated wore appreciated contemplated ...
VBG: verb, present participle or gerund
telegraphing stirring focusing angering judging stalling lactating
hankerin' alleging veering capping approaching traveling besieging
encrypting interrupting erasing wincing ...
VBN: verb, past participle
multihulled dilapidated aerosolized chaired languished panelized used
experimented flourished imitated reunifed factored condensed sheared
unsettled primed dubbed desired ...
VBP: verb, present tense, not 3rd person singular
predominate wrap resort sue twist spill cure lengthen brush terminate
appear tend stray glisten obtain comprise detest tease attract
emphasize mold postpone sever return wag ...
VBZ: verb, present tense, 3rd person singular
bases reconstructs marks mixes displeases seals carps weaves snatches
slumps stretches authorizes smolders pictures emerges stockpiles
seduces fizzes uses bolsters slaps speaks pleads ...
WDT: WH-determiner
that what whatever which whichever
WP: WH-pronoun
that what whatever whatsoever which who whom whosoever
WP$: WH-pronoun, possessive
whose
WRB: Wh-adverb
how however whence whenever where whereby whereever wherein whereof why
``: opening quotation mark
` ``
Patterns from Raw-Text Corpus#
import nltk
from nltk.corpus import gutenberg
gutenberg.fileids()
['austen-emma.txt',
'austen-persuasion.txt',
'austen-sense.txt',
'bible-kjv.txt',
'blake-poems.txt',
'bryant-stories.txt',
'burgess-busterbrown.txt',
'carroll-alice.txt',
'chesterton-ball.txt',
'chesterton-brown.txt',
'chesterton-thursday.txt',
'edgeworth-parents.txt',
'melville-moby_dick.txt',
'milton-paradise.txt',
'shakespeare-caesar.txt',
'shakespeare-hamlet.txt',
'shakespeare-macbeth.txt',
'whitman-leaves.txt']
alice_sents = [ ' '.join(sent)
for sent in gutenberg.sents(fileids='carroll-alice.txt')
if len(sent)>=5]
alice_sents[:5]
["[ Alice ' s Adventures in Wonderland by Lewis Carroll 1865 ]",
'Down the Rabbit - Hole',
"Alice was beginning to get very tired of sitting by her sister on the bank , and of having nothing to do : once or twice she had peeped into the book her sister was reading , but it had no pictures or conversations in it , ' and what is the use of a book ,' thought Alice ' without pictures or conversation ?'",
'So she was considering in her own mind ( as well as she could , for the hot day made her feel very sleepy and stupid ), whether the pleasure of making a daisy - chain would be worth the trouble of getting up and picking the daisies , when suddenly a White Rabbit with pink eyes ran close by her .',
"There was nothing so VERY remarkable in that ; nor did Alice think it so VERY much out of the way to hear the Rabbit say to itself , ' Oh dear !"]
import re
all_matches= [re.findall(r'(?:have|has)(?: [^s]+){0,2}[^\s]+(?:en|ed)', sent) for sent in alice_sents]
print([m for m in all_matches if len(m)!=0])
[['have wondered'], ['have been changed'], ['have been changed'], ['have dropped'], ['have changed'], ['have happened'], ['have liked'], ['have been changed'], ['have answered'], ['have got altered'], ['have called'], ['have to beat time when'], ["have done that , you know ,' Alice gently remarked ; ' they ' d have been"], ['have been'], ['have croqueted the Queen'], ['have everybody executed'], ['have any pepper in my kitchen'], ['have the experiment tried'], ['have been', 'have appeared'], ['have ordered'], ['have wanted'], ['have been'], ['have lived'], ['have no notion how delightful it will be When'], ['have baked'], ['have it explained'], ['have finished'], ['have you executed'], ['have you executed'], ['have you executed'], ['have been'], ['have imitated']]
The grouping parenthsis changes the behavior of
re.findall()With parenthesis, the regex engine automatically captures the matches in all the groups and return the results as a tuple.
Use
(?:...)to create non-capturing gorups
match = re.findall(r'(?:is|was) (?:\w+ing)', '''
Alice was beginning to get very tired of sitting by her sister on the bank ,
and of having nothing to do : once or twice she had peeped into the book
her sister was reading , but it had no pictures or conversations in it ,
and what is the use of a book , thought Alice without pictures or conversation ?
''')
if match:
for m in match:
print(m.strip())
was beginning
was reading
Important
It seems that when we use re.findall(), the matches returned would only be the capturing groups; but when we use re.finditer(), it would return the whole match strings as well as every section of the capturing groups.
I would prefer re.finditer(). Otherwise, re.findall() may need to specify the non-capturing group (?:...).
pat_perfect = re.compile(r'(is|was) (\w+ing)')
text = '''
Alice was beginning to get very tired of sitting by her sister on the bank ,
and of having nothing to do : once or twice she had peeped into the book
her sister was reading , but it had no pictures or conversations in it ,
and what is the use of a book , thought Alice without pictures or conversation ?
'''
pat_perfect_matches = pat_perfect.finditer(text)
if pat_perfect_matches:
for m in pat_perfect_matches:
print(m.group())
was beginning
was reading