Tokenize article into sentences. Tokenize each sentence in sentences into words using a list comprehension. Inside a lis

Here's the completed code: ``` # Import necessary modules import nltk from nltk.tokenize import sent_tokenize, word_tokenize # Tokenize the article into sentences: sentences sentences = sent_tokenize(article) # Tokenize each sentence into words: token_sentences token_sentences = [word_tokenize(sent) for sent in sentences] # Tag each tokenized sentence into parts of speech: pos_sentences pos_sentences = [nltk.pos_tag(sent) for sent in token_sentences] # Create the named entity chunks: chunked_sentences chunked_sentences = nltk.ne_chunk_sents(pos_sentences, binary=True) # Test for stems of the tree with 'NE' tags for sent in chunked_sentences: for chunk in sent: if hasattr(chunk, "label") and chunk.label() == "NE": print(chunk) ``` The code first imports `nltk` and `sent_tokenize`, `word_tokenize` from `nltk.tokenize`. It then tokenizes `article` into sentences using `sent_tokenize()` and assigns it to `sentences`. It tokenizes each sentence into words using a