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Python - Update tuple string element error

I have a dataframe where every row is a list of tuples , i.e.: tuple = (word, pos_tag). In each row, I want to change the word of some tuples by marking it and then update the tuple with the marked word. For example:
Initial dataframe row :
[('This', 'DET'), ('is', 'VERB'), ('an', 'DET'), ('example', 'NOUN'), ('text', 'NOUN'), ('that', 'DET'), ('I', 'PRON'), ('use', 'VERB'), ('in', 'ADP'), ('order', 'NOUN'), ('to', 'PART'), ('get', 'VERB'), ('an', 'DET'), ('answer', 'NOUN')]
Updated words :
updated_word : <IN>example</IN>
updated_word : <TAR>answer</TAR>
Desired output :
[('This', 'DET'), ('is', 'VERB'), ('an', 'DET'), ('<IN>example</IN>', 'NOUN'), ('text', 'NOUN'), ('that', 'DET'), ('I', 'PRON'), ('use', 'VERB'), ('in', 'ADP'), ('order', 'NOUN'), ('to', 'PART'), ('get', 'VERB'), ('an', 'DET'), ('<TAR>answer</TAR>', 'NOUN')]
But I get an error that TypeError: 'tuple' object is not callable. Can someone help? Here's the code :
for idx, row in df.iterrows():
doc = nlp(row['title'])
pos_tags = [(token.text, token.pos_) for token in doc if not token.pos_ == "PUNCT"]
for position, tuple in enumerate(pos_tags, start=1):
word = tuple[0]
spacy_pos_tag = tuple[1]
word = re.sub(r'[^\w\s]', '', word)
for col in cols:
if position in row[col]:
word = f'<{col.upper()}>{word}</{col.upper()}>'
else:
continue
tuple = tuple(word, spacy_pos_tag)
print(tuple)
>>>> Traceback (most recent call last):
>>>> tuple = tuple(word, spacy_pos_tag)
>>>> TypeError: 'tuple' object is not callable
Updated question
I have replaced tuple with tuple_ as suggested, but I still can't get back the desired output which is a list of tuples in every row. Can someone help how to update the dataframe rows? Here's the updated code :
for idx, row in df.iterrows():
doc = nlp(row['title'])
pos_tags = [(token.text, token.pos_) for token in doc if not token.pos_ == "PUNCT"]
# print(idx, "tokens, pos : ", pos_tags, "\n")
for position, tuple_ in enumerate(pos_tags, start=1):
word = tuple_[0]
spacy_pos_tag = tuple_[1]
word = re.sub(r'[^\w\s]', '', word)
for col in cols:
if position in row[col]:
word = f'<{col.upper()}>{word}</{col.upper()}>'
else:
continue
tuple_ = (word, spacy_pos_tag)
pos_tags.append(' '.join(position, tuple_))
# pos_tags.append(' '.join(tuple_))
print(idx, "tokens, pos : ", pos_tags, "\n")
>>>> Traceback (most recent call last):
>>>> pos_tag(df=df_matched)
>>>> pos_tags.append(' '.join(position, tuple_))
>>>> TypeError: join() takes exactly one argument (2 given)

Do not use tuple as a variable name, as it is a built-in python type name. Try the following instead:
for position, tuple_ in enumerate(pos_tags, start=1):
word = tuple_[0]
spacy_pos_tag = tuple_[1]
word = re.sub(r'[^\w\s]', '', word)
for col in cols:
if position in row[col]:
word = f'<{col.upper()}>{word}</{col.upper()}>'
else:
continue
tuple_ = (word, spacy_pos_tag)
print(tuple_)

Don't use "tuple" as name of a variable. It's a type name

None result in the grouped method in Python coding

I am trying to get result as a printed sentence in my NER code:
[(Token_1, PoS_1, Tag_1), ..., (Token_n, PoS_n, Tag_n)]
In the function 'get_next' I am getting 'None' result every time - not sure what I am doing wrong here. Code:
class SentenceGetter(object):
def __init__(self, data):
self.n_sent = 1
self.data = data
self.empty = False
agg_func = lambda s: [(w, p, t) for w, p, t in zip(s["Lemat"].values.tolist(),
s["POS"].values.tolist(),
s["TAG"].values.tolist())]
self.grouped = self.data.groupby("Forma").apply(agg_func)
self.sentences = [s for s in self.grouped]
def get_next(self):
try:
s = self.grouped["Forma: {}".format(self.n_sent)]
self.n_sent += 1
return s
except:
return None
getter = SentenceGetter(data)
sent = getter.get_next()
print('Example sentence:')
print(sent)
Code block about definition:
data = pd.read_csv("nkjp-morph-named.txt",delimiter="\t")
data = data.fillna(method="ffill")
print("Form number: ", len(data.groupby(['Forma'])))
lemats = list(set(data["Lemat"].values))
n_lemats = len(lemats)
print("Lemats: ", n_lemats)
tags = list(set(data["TAG"].values))
print("TAG:", tags)
n_tags = len(tags)
print("Number of TAGs: ", n_tags)
print("Dataset:")
data.head(n=16)
Could you please help me to understand it more why its None still??
Excepted result:
Example sentence: [('Thousands', 'NNS', 'O'), ('of', 'IN', 'O'),
('demonstrators', 'NNS', 'O'), ('have', 'VBP', 'O'), ('marched',
'VBN', 'O'), ('through', 'IN', 'O'), ('London', 'NNP', 'B-geo'),
('to', 'TO', 'O'), ('protest', 'VB', 'O'), ('the', 'DT', 'O'), ('war',
'NN', 'O'), ('in', 'IN', 'O'), ('Iraq', 'NNP', 'B-geo'), ('and', 'CC',
'O'), ('demand', 'VB', 'O'), ('the', 'DT', 'O'), ('withdrawal', 'NN',
'O'), ('of', 'IN', 'O'), ('British', 'JJ', 'B-gpe'), ('troops', 'NNS',
'O'), ('from', 'IN', 'O'), ('that', 'DT', 'O'), ('country', 'NN',
'O'), ('.', '.', 'O')]

Lemmatize string according to pos nlp

I'm trying to lemmatize a string according to the part of speech but at the final stage, i'm getting an error. My code:
import nltk
from nltk.stem import *
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import wordnet
wordnet_lemmatizer = WordNetLemmatizer()
text = word_tokenize('People who help the blinging lights are the way of the future and are heading properly to their goals')
tagged = nltk.pos_tag(text)
def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
elif treebank_tag.startswith('N'):
return wordnet.NOUN
elif treebank_tag.startswith('R'):
return wordnet.ADV
else:
return ''
for word in tagged: print(wordnet_lemmatizer.lemmatize(word,pos='v'), end=" ")
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-40-afb22c78f770> in <module>()
----> 1 for word in tagged: print(wordnet_lemmatizer.lemmatize(word,pos='v'), end=" ")
E:\Miniconda3\envs\uol1\lib\site-packages\nltk\stem\wordnet.py in lemmatize(self, word, pos)
38
39 def lemmatize(self, word, pos=NOUN):
---> 40 lemmas = wordnet._morphy(word, pos)
41 return min(lemmas, key=len) if lemmas else word
42
E:\Miniconda3\envs\uol1\lib\site-packages\nltk\corpus\reader\wordnet.py in _morphy(self, form, pos)
1710
1711 # 1. Apply rules once to the input to get y1, y2, y3, etc.
-> 1712 forms = apply_rules([form])
1713
1714 # 2. Return all that are in the database (and check the original too)
E:\Miniconda3\envs\uol1\lib\site-packages\nltk\corpus\reader\wordnet.py in apply_rules(forms)
1690 def apply_rules(forms):
1691 return [form[:-len(old)] + new
-> 1692 for form in forms
1693 for old, new in substitutions
1694 if form.endswith(old)]
E:\Miniconda3\envs\uol1\lib\site-packages\nltk\corpus\reader\wordnet.py in <listcomp>(.0)
1692 for form in forms
1693 for old, new in substitutions
-> 1694 if form.endswith(old)]
1695
1696 def filter_forms(forms):
I want to be able to lemmatize that string based on each word's part of speech all at once. Please help.

Firstly, try not to mix top-level, absolute and relative imports like these:
import nltk
from nltk.stem import *
from nltk import pos_tag, word_tokenize
This would be better:
from nltk import sent_tokenize, word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet as wn
(See Absolute vs. explicit relative import of Python module)
The error you're getting is most probably because you are feeding in the outputs of pos_tag as the input to the WordNetLemmatizer.lemmatize(), i.e. :
>>> from nltk import pos_tag
>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> sent = 'People who help the blinging lights are the way of the future and are heading properly to their goals'.split()
>>> pos_tag(sent)
[('People', 'NNS'), ('who', 'WP'), ('help', 'VBP'), ('the', 'DT'), ('blinging', 'NN'), ('lights', 'NNS'), ('are', 'VBP'), ('the', 'DT'), ('way', 'NN'), ('of', 'IN'), ('the', 'DT'), ('future', 'NN'), ('and', 'CC'), ('are', 'VBP'), ('heading', 'VBG'), ('properly', 'RB'), ('to', 'TO'), ('their', 'PRP$'), ('goals', 'NNS')]
>>> pos_tag(sent)[0]
('People', 'NNS')
>>> first_word = pos_tag(sent)[0]
>>> wnl.lemmatize(first_word)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/stem/wordnet.py", line 40, in lemmatize
lemmas = wordnet._morphy(word, pos)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1712, in _morphy
forms = apply_rules([form])
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1694, in apply_rules
if form.endswith(old)]
AttributeError: 'tuple' object has no attribute 'endswith'
The input to WordNetLemmatizer.lemmatize() should be str not a tuple, so if you do:
>>> tagged_sent = pos_tag(sent)
>>> def penn2morphy(penntag, returnNone=False):
... morphy_tag = {'NN':wn.NOUN, 'JJ':wn.ADJ,
... 'VB':wn.VERB, 'RB':wn.ADV}
... try:
... return morphy_tag[penntag[:2]]
... except:
... return None if returnNone else ''
...
>>> for word, tag in tagged_sent:
... wntag = penn2morphy(tag)
... if wntag:
... print wnl.lemmatize(word, pos=wntag)
... else:
... print word
...
People
who
help
the
blinging
light
be
the
way
of
the
future
and
be
head
properly
to
their
goal
Or if you like an easy way out:
pip install pywsd
Then:
>>> from pywsd.utils import lemmatize, lemmatize_sentence
>>> sent = 'People who help the blinging lights are the way of the future and are heading properly to their goals'
>>> lemmatize_sentence(sent)
['people', 'who', 'help', 'the', u'bling', u'light', u'be', 'the', 'way', 'of', 'the', 'future', 'and', u'be', u'head', 'properly', 'to', 'their', u'goal']

How to extract chunks from BIO chunked sentences? - python

Give an input sentence, that has BIO chunk tags:
[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed',
'I-NP'), ('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'),
('swallow', 'I-NP'), ('?', 'O')]
I would need to extract the relevant phrases out, e.g. if I want to extract 'NP', I would need to extract the fragments of tuples that contains B-NP and I-NP.
[out]:
[('What', '0'), ('the airspeed', '2-3'), ('an unladen swallow', '5-6-7')]
(Note: the numbers in the extract tuples represent the token index.)
I have tried extracting it using the following code:
def extract_chunks(tagged_sent, chunk_type):
current_chunk = []
current_chunk_position = []
for idx, word_pos in enumerate(tagged_sent):
word, pos = word_pos
if '-'+chunk_type in pos: # Append the word to the current_chunk.
current_chunk.append((word))
current_chunk_position.append((idx))
else:
if current_chunk: # Flush the full chunk when out of an NP.
_chunk_str = ' '.join(current_chunk)
_chunk_pos_str = '-'.join(map(str, current_chunk_position))
yield _chunk_str, _chunk_pos_str
current_chunk = []
current_chunk_position = []
if current_chunk: # Flush the last chunk.
yield ' '.join(current_chunk), '-'.join(current_chunk_position)
tagged_sent = [('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'), ('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'), ('?', 'O')]
print (list(extract_chunks(tagged_sent, chunk_type='NP')))
But when I have adjacent chunk of the same type:
tagged_sent = [('The', 'B-NP'), ('Mitsubishi', 'I-NP'), ('Electric', 'I-NP'), ('Company', 'I-NP'), ('Managing', 'B-NP'), ('Director', 'I-NP'), ('ate', 'B-VP'), ('ramen', 'B-NP')]
print (list(extract_chunks(tagged_sent, chunk_type='NP')))
It outputs this:
[('The Mitsubishi Electric Company Managing Director', '0-1-2-3-4-5'), ('ramen', '7')]
Instead of the desired:
[('The Mitsubishi Electric Company', '0-1-2-3'), ('Managing Director', '4-5'), ('ramen', '7')]
How can this be resolved from the above code?
Other than how it's done from the code above, is there a better solution to extract the desired chunks of a specific chunk_type?

Try this, it will extract all types of chunks with the indices of their respective words.
def extract_chunks(tagged_sent, chunk_type='NP'):
out_sen = []
for idx, word_pos in enumerate(tagged_sent):
word,bio = word_pos
boundary,tag = bio.split("-") if "-" in bio else ('','O')
if tag != chunk_type:continue
if boundary == "B":
out_sen.append([word, str(idx)])
elif boundary == "I":
out_sen[-1][0] += " "+ word
out_sen[-1][-1] += "-"+ str(idx)
else:
out_sen.append([word, str(idx)])
return out_sen
Demo:
>>> tagged_sent = [('The', 'B-NP'), ('Mitsubishi', 'I-NP'), ('Electric', 'I-NP'), ('Company', 'I-NP'), ('Managing', 'B-NP'), ('Director', 'I-NP'), ('ate', 'B-VP'), ('ramen', 'B-NP')]
>>> output_sent = extract_chunks(tagged_sent)
>>> print map(tuple, output_sent)
[('The Mitsubishi Electric Company', '0-1-2-3'), ('Managing Director', '4-5'), ('ramen', '7')]

def extract_chunks(tagged_sent, chunk_type):
grp1, grp2, chunk_type = [], [], "-" + chunk_type
for ind, (s, tp) in enumerate(tagged_sent):
if tp.endswith(chunk_type):
if not tp.startswith("B"):
grp2.append(str(ind))
grp1.append(s)
else:
if grp1:
yield " ".join(grp1), "-".join(grp2)
grp1, grp2 = [s], [str(ind)]
yield " ".join(grp1), "-".join(grp2)
Output:
In [2]: l = [('The', 'B-NP'), ('Mitsubishi', 'I-NP'), ('Electric', 'I-NP'), ('Company', 'I-NP'), ('Managing', 'B-NP'),
...: ('Director', 'I-NP'), ('ate', 'B-VP'), ('ramen', 'B-NP')]
In [3]: list(extract_chunks(l, "NP"))
Out[3]:
[('The Mitsubishi Electric Company', '0-1-2-3'),
('Managing Director', '4-5'),
('ramen', '7')]
In [4]: l = [('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'), ('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'), ('?', 'O')]
In [5]: list(extract_chunks(l, "NP"))
Out[5]: [('What', '0'), ('the airspeed', '2-3'), ('an unladen swallow', '5-6-7')]

I would do it like this:
import re
def extract_chunks(tagged_sent, chunk_type):
# compiles the expression we want to match
regex = re.compile(chunk_type)
# filters matched items in a dictionary whose keys are the matched indexes
first_step = {index_:tag[0] for index_, tag in enumerate(tagged_sent) if regex.findall(tag[1])}
# builds list of lists following output format
second_step = []
for key_ in sorted(first_step.keys()):
if second_step and int(second_step [len(second_step )-1][1].split('-')[-1]) == key_ -1:
second_step[len(second_step)-1][0] += ' {0}'.format(first_step[key_])
second_step[len(second_step)-1][1] += '-{0}'.format(str(key_))
else:
second_step.append([first_step[key_], str(key_)])
# builds output in final format
return [tuple(item) for item in second_step]
You can adapt it to use generators instead of building the whole output in memory like I am doing and refactory it for better performance (I'm in a hurry so the code is far from optimal).
Hope it helps!

Why can't I pass wn.ADJ_SAT as a pos when requesting synsets

I know that wordnet has an "adverb synset" type. I know that is in the synset type enum in nltk
from nltk.corpus import wordnet as wn
wn.ADJ_SAT
u's'
Why can't I pass it as a key to synsets?
>>> wn.synsets('dog', wn.ADJ_SAT)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/nltk/corpus/reader/wordnet.py", line 1413, in synsets
for form in self._morphy(lemma, p)
File "/Library/Python/2.7/site-packages/nltk/corpus/reader/wordnet.py", line 1627, in _morphy
substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos]
KeyError: u's'

From:
>>> from nltk.corpus import wordnet as wn
>>> wn.synsets('able')
[Synset('able.a.01'), Synset('able.s.02'), Synset('able.s.03'), Synset('able.s.04')]
>>> wn.synsets('able', pos=wn.ADJ)
[Synset('able.a.01'), Synset('able.s.02'), Synset('able.s.03'), Synset('able.s.04')]
>>> wn.synsets('able', pos=wn.ADJ_SAT)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1413, in synsets
for form in self._morphy(lemma, p)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1627, in _morphy
substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos]
KeyError: u's'
From https://github.com/nltk/nltk/blob/develop/nltk/corpus/reader/wordnet.py#L1397 , we see that when you try to retrieve a synset from the NLTK wordnet API, the POS restrictions appears in the return list comprehension that calls the self._morphy(lemma, p) function:
def synsets(self, lemma, pos=None, lang='en'):
"""Load all synsets with a given lemma and part of speech tag.
If no pos is specified, all synsets for all parts of speech
will be loaded.
If lang is specified, all the synsets associated with the lemma name
of that language will be returned.
"""
lemma = lemma.lower()
if lang == 'en':
get_synset = self._synset_from_pos_and_offset
index = self._lemma_pos_offset_map
if pos is None:
pos = POS_LIST
return [get_synset(p, offset)
for p in pos
for form in self._morphy(lemma, p)
for offset in index[form].get(p, [])]
If we look at the _morphy() function, from https://github.com/nltk/nltk/blob/develop/nltk/corpus/reader/wordnet.py#L1573.
def _morphy(self, form, pos):
# from jordanbg:
# Given an original string x
# 1. Apply rules once to the input to get y1, y2, y3, etc.
# 2. Return all that are in the database
# 3. If there are no matches, keep applying rules until you either
# find a match or you can't go any further
exceptions = self._exception_map[pos]
substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos]
def apply_rules(forms):
return [form[:-len(old)] + new
for form in forms
for old, new in substitutions
if form.endswith(old)]
def filter_forms(forms):
result = []
seen = set()
for form in forms:
if form in self._lemma_pos_offset_map:
if pos in self._lemma_pos_offset_map[form]:
if form not in seen:
result.append(form)
seen.add(form)
return result
# 0. Check the exception lists
if form in exceptions:
return filter_forms([form] + exceptions[form])
# 1. Apply rules once to the input to get y1, y2, y3, etc.
forms = apply_rules([form])
# 2. Return all that are in the database (and check the original too)
results = filter_forms([form] + forms)
if results:
return results
# 3. If there are no matches, keep applying rules until we find a match
while forms:
forms = apply_rules(forms)
results = filter_forms(forms)
if results:
return results
# Return an empty list if we can't find anything
return []
We see that it retrieves some substitution rules from substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos] to perform some morphological reduction before it retrieves the Synsets that are stored in the "based"/"root" form. E.g.
>>> from nltk.corpus import wordnet as wn
>>> wn._morphy('dogs', 'n')
[u'dog']
And if we look at the MORPHOLOGICAL_SUBSTITUTIONS, we see that ADJ_SAT is missing, see https://github.com/nltk/nltk/blob/develop/nltk/corpus/reader/wordnet.py#L1609 :
MORPHOLOGICAL_SUBSTITUTIONS = {
NOUN: [('s', ''), ('ses', 's'), ('ves', 'f'), ('xes', 'x'),
('zes', 'z'), ('ches', 'ch'), ('shes', 'sh'),
('men', 'man'), ('ies', 'y')],
VERB: [('s', ''), ('ies', 'y'), ('es', 'e'), ('es', ''),
('ed', 'e'), ('ed', ''), ('ing', 'e'), ('ing', '')],
ADJ: [('er', ''), ('est', ''), ('er', 'e'), ('est', 'e')],
ADV: []}
Thus to prevent this from happening a simple fix to add this line after line 1609 of https://github.com/nltk/nltk/blob/develop/nltk/corpus/reader/wordnet.py#L1609:
MORPHOLOGICAL_SUBSTITUTIONS[ADJ_SAT] = MORPHOLOGICAL_SUBSTITUTIONS[ADJ]
For proof of concept:
>>> MORPHOLOGICAL_SUBSTITUTIONS = {
... 1: [('s', ''), ('ses', 's'), ('ves', 'f'), ('xes', 'x'),
... ('zes', 'z'), ('ches', 'ch'), ('shes', 'sh'),
... ('men', 'man'), ('ies', 'y')],
... 2: [('s', ''), ('ies', 'y'), ('es', 'e'), ('es', ''),
... ('ed', 'e'), ('ed', ''), ('ing', 'e'), ('ing', '')],
... 3: [('er', ''), ('est', ''), ('er', 'e'), ('est', 'e')],
... 4: []}
>>>
>>> MORPHOLOGICAL_SUBSTITUTIONS[5] = MORPHOLOGICAL_SUBSTITUTIONS[3]
>>> MORPHOLOGICAL_SUBSTITUTIONS
{1: [('s', ''), ('ses', 's'), ('ves', 'f'), ('xes', 'x'), ('zes', 'z'), ('ches', 'ch'), ('shes', 'sh'), ('men', 'man'), ('ies', 'y')], 2: [('s', ''), ('ies', 'y'), ('es', 'e'), ('es', ''), ('ed', 'e'), ('ed', ''), ('ing', 'e'), ('ing', '')], 3: [('er', ''), ('est', ''), ('er', 'e'), ('est', 'e')], 4: [], 5: [('er', ''), ('est', ''), ('er', 'e'), ('est', 'e')]}

Thanks for the suggestion, adopted in https://github.com/nltk/nltk/commit/21fd0c538b0144c5f722e4f643c9ced1deb1f15e