In situations where you wish to POS tag a column of text stored in a pandas dataframe with 1 sentence per row the majority of implementations on SO use the apply method
dfData['POSTags']= dfData['SourceText'].apply(
lamda row: [pos_tag(word_tokenize(row) for item in row])
The NLTK documentation recommends using the pos_tag_sents() for efficient tagging of more than one sentence.
Does that apply to this example and if so would the code be as simple as changing pso_tag to pos_tag_sents or does NLTK mean text sources of paragraphs
As mentioned in the comments pos_tag_sents() aims to reduce the loading of the preceptor each time but the issue is how to do this and still produce a column in a pandas dataframe?
Input
$ cat test.csv
ID,Task,label,Text
1,Collect Information,no response,cozily married practical athletics Mr. Brown flat
2,New Credit,no response,active married expensive soccer Mr. Chang flat
3,Collect Information,response,healthy single expensive badminton Mrs. Green flat
4,Collect Information,response,cozily married practical soccer Mr. Brown hierachical
5,Collect Information,response,cozily single practical badminton Mr. Brown flat
TL;DR
>>> from nltk import word_tokenize, pos_tag, pos_tag_sents
>>> import pandas as pd
>>> df = pd.read_csv('test.csv', sep=',')
>>> df['Text']
0 cozily married practical athletics Mr. Brown flat
1 active married expensive soccer Mr. Chang flat
2 healthy single expensive badminton Mrs. Green ...
3 cozily married practical soccer Mr. Brown hier...
4 cozily single practical badminton Mr. Brown flat
Name: Text, dtype: object
>>> texts = df['Text'].tolist()
>>> tagged_texts = pos_tag_sents(map(word_tokenize, texts))
>>> tagged_texts
[[('cozily', 'RB'), ('married', 'JJ'), ('practical', 'JJ'), ('athletics', 'NNS'), ('Mr.', 'NNP'), ('Brown', 'NNP'), ('flat', 'JJ')], [('active', 'JJ'), ('married', 'VBD'), ('expensive', 'JJ'), ('soccer', 'NN'), ('Mr.', 'NNP'), ('Chang', 'NNP'), ('flat', 'JJ')], [('healthy', 'JJ'), ('single', 'JJ'), ('expensive', 'JJ'), ('badminton', 'NN'), ('Mrs.', 'NNP'), ('Green', 'NNP'), ('flat', 'JJ')], [('cozily', 'RB'), ('married', 'JJ'), ('practical', 'JJ'), ('soccer', 'NN'), ('Mr.', 'NNP'), ('Brown', 'NNP'), ('hierachical', 'JJ')], [('cozily', 'RB'), ('single', 'JJ'), ('practical', 'JJ'), ('badminton', 'NN'), ('Mr.', 'NNP'), ('Brown', 'NNP'), ('flat', 'JJ')]]
>>> df['POS'] = tagged_texts
>>> df
ID Task label \
0 1 Collect Information no response
1 2 New Credit no response
2 3 Collect Information response
3 4 Collect Information response
4 5 Collect Information response
Text \
0 cozily married practical athletics Mr. Brown flat
1 active married expensive soccer Mr. Chang flat
2 healthy single expensive badminton Mrs. Green ...
3 cozily married practical soccer Mr. Brown hier...
4 cozily single practical badminton Mr. Brown flat
POS
0 [(cozily, RB), (married, JJ), (practical, JJ),...
1 [(active, JJ), (married, VBD), (expensive, JJ)...
2 [(healthy, JJ), (single, JJ), (expensive, JJ),...
3 [(cozily, RB), (married, JJ), (practical, JJ),...
4 [(cozily, RB), (single, JJ), (practical, JJ), ...
In Long:
First, you can extract the Text column to a list of string:
texts = df['Text'].tolist()
Then you can apply the word_tokenize function:
map(word_tokenize, texts)
Note that, @Boud's suggested is almost the same, using df.apply:
df['Text'].apply(word_tokenize)
Then you dump the tokenized text into a list of list of string:
df['Text'].apply(word_tokenize).tolist()
Then you can use pos_tag_sents:
pos_tag_sents( df['Text'].apply(word_tokenize).tolist() )
Then you add the column back to the DataFrame:
df['POS'] = pos_tag_sents( df['Text'].apply(word_tokenize).tolist() )