def demo():
from pprint import pprint
from nltk.corpus import framenet as fn
#
# It is not necessary to explicitly build the indexes by calling
# buildindexes(). We do this here just for demo purposes. If the
# indexes are not built explicitely, they will be built as needed.
#
print('Building the indexes...')
fn.buildindexes()
#
# Get some statistics about the corpus
#
print('Number of Frames:', len(fn.frames()))
print('Number of Lexical Units:', len(fn.lexical_units()))
print('Number of annotated documents:', len(fn.documents()))
print()
#
# Frames
#
print('getting frames whose name matches the (case insensitive) regex: "(?i)medical"')
medframes = fn.frames(r'(?i)medical')
print(
'Found {0} Frames whose name matches "(?i)medical":'.format(len(medframes)))
print([(f.name, f.ID) for f in medframes])
#
# store the first frame in the list of frames
#
tmp_id = medframes[0].ID
m_frame = fn.frame(tmp_id) # reads all info for the frame
#
# get the frame relations
#
print(
'\nNumber of frame relations for the "{0}" ({1}) frame:'.format(m_frame.name,
m_frame.ID),
len(m_frame.frameRelation))
for fr in m_frame.frameRelation:
print(' ', fr.type + ":", fr.relatedFrame)
#
# get the names of the Frame Elements
#
print(
'\nNumber of Frame Elements in the "{0}" frame:'.format(m_frame.name),
len(m_frame.FE))
print(' ', [x.name for x in m_frame.FE])
#
# get the names of the "Core" Frame Elements
#
print(
'\nThe "core" Frame Elements in the "{0}" frame:'.format(m_frame.name))
print(' ', [x.name for x in m_frame.FE if x.coreType == "Core"])
#
# get all of the Lexical Units that are incorporated in the
# 'Ailment' FE of the 'Medical_conditions' frame (id=239)
#
print('\nAll Lexical Units that are incorporated in the "Ailment" FE:')
m_frame = fn.frame(239)
ailment_lus = [x for x in m_frame.lexUnit if x.incorporatedFE == 'Ailment']
print([x.name for x in ailment_lus])
#
# get all of the Lexical Units for the frame
#
print('\nNumber of Lexical Units in the "{0}" frame:'.format(m_frame.name),
len(m_frame.lexUnit))
print(' ', [x.name for x in m_frame.lexUnit[:5]], '...')
#
# get basic info on the second LU in the frame
#
tmp_id = m_frame.lexUnit[1].ID # grab the id of the second LU
luinfo = fn.lu_basic(tmp_id) # get basic info on the LU
print('\nInformation on the LU: {0}'.format(luinfo.name))
pprint(luinfo)
#
# Get a list of all of the corpora used for fulltext annotation
#
print('\nNames of all of the corpora used for fulltext annotation:')
allcorpora = set([x.corpname for x in fn.documents()])
pprint(list(allcorpora))
#
# Get the names of the annotated documents in the first corpus
#
firstcorp = list(allcorpora)[0]
firstcorp_docs = fn.documents(firstcorp)
print(
'\nNames of the annotated documents in the "{0}" corpus:'.format(firstcorp))
pprint([x.filename for x in firstcorp_docs])
#
# Search for frames containing LUs whose name attribute matches a
# regexp pattern.
#
# Note: if you were going to be doing a lot of this type of
# searching, you'd want to build an index that maps from
# lemmas to frames because each time frames_by_lemma() is
# called, it has to search through ALL of the frame XML files
# in the db.
print('\nSearching for all Frames that have a lemma that matches the regexp: "^run.v$":')
pprint(fn.frames_by_lemma(r'^run.v$'))
def demo():
from pprint import pprint
from nltk.corpus import framenet as fn
#
# It is not necessary to explicitly build the indexes by calling
# buildindexes(). We do this here just for demo purposes. If the
# indexes are not built explicitely, they will be built as needed.
#
print('Building the indexes...')
fn.buildindexes()
#
# Get some statistics about the corpus
#
print('Number of Frames:', len(fn.frames()))
print('Number of Lexical Units:', len(fn.lexical_units()))
print('Number of annotated documents:', len(fn.documents()))
print()
#
# Frames
#
print(
'getting frames whose name matches the (case insensitive) regex: "(?i)medical"'
)
medframes = fn.frames(r'(?i)medical')
print('Found {0} Frames whose name matches "(?i)medical":'.format(
len(medframes)))
print([(f.name, f.ID) for f in medframes])
#
# store the first frame in the list of frames
#
tmp_id = medframes[0].ID
m_frame = fn.frame(tmp_id) # reads all info for the frame
#
# get the frame relations
#
print(
'\nNumber of frame relations for the "{0}" ({1}) frame:'.format(
m_frame.name, m_frame.ID), len(m_frame.frameRelation))
for fr in m_frame.frameRelation:
print(' ', fr.type + ":", fr.relatedFrame)
#
# get the names of the Frame Elements
#
print(
'\nNumber of Frame Elements in the "{0}" frame:'.format(m_frame.name),
len(m_frame.FE))
print(' ', [x.name for x in m_frame.FE])
#
# get the names of the "Core" Frame Elements
#
print('\nThe "core" Frame Elements in the "{0}" frame:'.format(
m_frame.name))
print(' ', [x.name for x in m_frame.FE if x.coreType == "Core"])
#
# get all of the Lexical Units that are incorporated in the
# 'Ailment' FE of the 'Medical_conditions' frame (id=239)
#
print('\nAll Lexical Units that are incorporated in the "Ailment" FE:')
m_frame = fn.frame(239)
ailment_lus = [x for x in m_frame.lexUnit if x.incorporatedFE == 'Ailment']
print([x.name for x in ailment_lus])
#
# get all of the Lexical Units for the frame
#
print('\nNumber of Lexical Units in the "{0}" frame:'.format(m_frame.name),
len(m_frame.lexUnit))
print(' ', [x.name for x in m_frame.lexUnit[:5]], '...')
#
# get basic info on the second LU in the frame
#
tmp_id = m_frame.lexUnit[1].ID # grab the id of the second LU
luinfo = fn.lu_basic(tmp_id) # get basic info on the LU
print('\nInformation on the LU: {0}'.format(luinfo.name))
pprint(luinfo)
#
# Get a list of all of the corpora used for fulltext annotation
#
print('\nNames of all of the corpora used for fulltext annotation:')
allcorpora = set([x.corpname for x in fn.documents()])
pprint(list(allcorpora))
#
# Get the names of the annotated documents in the first corpus
#
firstcorp = list(allcorpora)[0]
firstcorp_docs = fn.documents(firstcorp)
print('\nNames of the annotated documents in the "{0}" corpus:'.format(
firstcorp))
pprint([x.filename for x in firstcorp_docs])
#
# Search for frames containing LUs whose name attribute matches a
# regexp pattern.
#
# Note: if you were going to be doing a lot of this type of
# searching, you'd want to build an index that maps from
# lemmas to frames because each time frames_by_lemma() is
# called, it has to search through ALL of the frame XML files
# in the db.
print(
'\nSearching for all Frames that have a lemma that matches the regexp: "^run.v$":'
)
pprint(fn.frames_by_lemma(r'^run.v$'))
for f in fn.lus('look.n'):
print f.frame.name
result = fn.frames(r'(?i)erception')
print result
f = fn.frame(1301)
f.ID
f.definition
for u in f.lexUnit:
print u
fn.lexical_units('r(?i)look')
from pattern.en import wordnet
[x for x in f.FE]
f.frameRelations
all_lu = set()
for f in fn.frames():
lus = [ lu.split('.')[1] for lu in fn.frame(f.ID).lexUnit ]
for lu in lus:
all_lu.add(lu)
lmtzr.lemmatize('humidity')
from nltk.stem.lancaster import LancasterStemmer
st = LancasterStemmer()
st.stem('luminous')
lemma('humidity')
frames = fn.frames_by_lemma(r'skin')
for f in frames:
print '%s - %s\n' % (f.name, f.definition)
fn.lexical_units(r'')
fn.frames_by_lemma(r'(?i)a little')
for f in ('reflect', 'bank'):
taxonomy.append(f, type='angle')
for f in ('bank', 'financial-institution'):
taxonomy.append(f, type='finance')
t = parsetree('A field of daffodils is white.', lemmata=True)