NER with NLTK in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize the sentence before named entity recognition.

NLP

import nltk
sentence = "Apple is looking at buying U.K. startup for $1 billion"
tokens = nltk.word_tokenize([1])
print(tokens)

Drag options to blanks, or click blank then click option'

Atext

Bnltk

Ctokens

Dsentence

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the tokenizer function itself instead of the sentence string.

Passing the token list instead of the sentence string.

The word_tokenize function needs the sentence string as input to split it into words.

2fill in blank

medium

Complete the code to tag parts of speech for the tokens.

NLP

pos_tags = nltk.pos_tag([1])
print(pos_tags)

Drag options to blanks, or click blank then click option'

Atokens

Bsentence

Cpos_tags

Dnltk

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original sentence string instead of tokens.

Passing the POS tags variable itself.

The pos_tag function requires a list of tokens to assign part-of-speech tags.

3fill in blank

hard

Fix the error in the code to perform named entity recognition on POS-tagged tokens.

NLP

named_entities = nltk.ne_chunk([1])
print(named_entities)

Drag options to blanks, or click blank then click option'

Asentence

Bpos_tags

Ctokens

Dnamed_entities

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw tokens instead of POS-tagged tokens.

Passing the original sentence string.

The ne_chunk function requires POS-tagged tokens as input to identify named entities.

4fill in blank

hard

Fill both blanks to extract named entity labels and their word tokens from the tree.

NLP

for subtree in named_entities:
    if hasattr(subtree, '[1]') and subtree.label() == '[2]':
        print('Entity:', ' '.join([token for token, pos in subtree.leaves()]))

Drag options to blanks, or click blank then click option'

Alabel

Blabel()

CPERSON

Dleaves

Attempts:

3 left

💡 Hint

Common Mistakes

Using label() inside hasattr which expects a string attribute name.

Confusing method call with attribute name.

We check if the subtree has the label attribute and compare it to the entity type string, e.g., PERSON. Here, the blank expects the attribute name label.

5fill in blank

hard

Fill all three blanks to create a dictionary of named entities and their types.

NLP

entities = { ' '.join([token for token, pos in subtree.leaves()]): subtree.[1]() for subtree in named_entities if hasattr(subtree, '[2]') }
print(entities)

# Filter only entities of type [3]

Drag options to blanks, or click blank then click option'

Alabel

CPERSON

Dleaves

Attempts:

3 left

💡 Hint

Common Mistakes

Using leaves instead of label for entity type.

Passing method call as string in hasattr.

Using wrong entity type string.

The dictionary comprehension extracts entity names as keys and their labels as values. We check for the label attribute and call label() to get the entity type. Finally, we filter for entities of type PERSON.

Practice

1. What is the main purpose of Named Entity Recognition (NER) in Natural Language Processing?

easy

A. To count the number of words in a sentence

B. To translate text from one language to another

C. To find names of people, places, and organizations in text

D. To correct spelling mistakes in text

Solution

1. Step 1: Understand NER's role

   NER is designed to identify and classify named entities like people, places, and organizations in text.

2. Step 2: Compare with other NLP tasks

   Translation, word counting, and spell checking are different tasks unrelated to NER.

3. Final Answer:

   To find names of people, places, and organizations in text -> Option C

4. Quick Check:

   NER = Find names [OK]

Hint: NER extracts names and places from text quickly [OK]

Common Mistakes:

• Confusing NER with translation
• Thinking NER counts words
• Mixing NER with spell checking

2. Which NLTK function is used to perform Named Entity Recognition after POS tagging?

easy

A. ne_chunk()

B. word_tokenize()

C. pos_tag()

D. sent_tokenize()

Solution

1. Step 1: Identify NLTK functions for NER

   NLTK uses ne_chunk() to recognize named entities from POS-tagged tokens.

2. Step 2: Differentiate from other functions

   word_tokenize() splits text into words, pos_tag() tags parts of speech, and sent_tokenize() splits text into sentences.

3. Final Answer:

   ne_chunk() -> Option A

4. Quick Check:

   NER uses ne_chunk() [OK]

Hint: Use ne_chunk() after pos_tag() for NER in NLTK [OK]

Common Mistakes:

• Using word_tokenize() for NER
• Confusing pos_tag() with NER
• Trying sent_tokenize() for entity recognition

3. What will be the output type of ne_chunk(pos_tag(word_tokenize(text))) in NLTK?

medium

A. A plain string with entity labels

B. A list of strings

C. A dictionary mapping words to entity types

D. A tree structure with named entities as subtrees

Solution

1. Step 1: Understand ne_chunk output

   The ne_chunk() function returns a tree structure where named entities are subtrees labeled with entity types.

2. Step 2: Compare output types

   It is not a list, dictionary, or plain string but a hierarchical tree that can be traversed.

3. Final Answer:

   A tree structure with named entities as subtrees -> Option D

4. Quick Check:

   ne_chunk output = tree structure [OK]

Hint: ne_chunk returns a tree, not a list or dict [OK]

Common Mistakes:

• Expecting a list of strings
• Thinking output is a dictionary
• Assuming output is a plain string

4. Given the code snippet:

import nltk
text = "Apple is looking at buying U.K. startup"
tokens = nltk.word_tokenize(text)
pos_tags = nltk.pos_tag(tokens)
entities = nltk.ne_chunk(pos_tags, binary=True)
print(entities)

What is the likely error in this code?

medium

A. Missing import for ne_chunk

B. Incorrect argument 'binary=True' in ne_chunk

C. pos_tag requires a list of sentences, not tokens

D. word_tokenize should be called after ne_chunk

Solution

1. Step 1: Check ne_chunk parameters

   The ne_chunk() function's binary=True limits it to binary NER (labels entities simply as NE, typically focusing on PERSON), which is incorrect for standard NER requiring specific types like PERSON, ORGANIZATION, GPE.

2. Step 2: Verify other parts

   Imports are correct with import nltk, pos_tag() accepts tokenized words, and preprocessing order is proper.

3. Final Answer:

   Incorrect argument 'binary=True' in ne_chunk -> Option B

4. Quick Check:

   binary=True limits to binary NER [OK]

Hint: Use binary=False for detailed entity types in ne_chunk [OK]

Common Mistakes:

• Using binary=True for detailed NER
• Calling word_tokenize after ne_chunk
• Misunderstanding pos_tag input

5. You want to extract only PERSON entities from a text using NLTK's ne_chunk. Which approach correctly filters PERSON entities from the chunked tree?

hard

A. Traverse the tree and select subtrees with label 'PERSON'

B. Use pos_tag to find tokens tagged as 'PERSON'

C. Filter tokens containing capital letters only

D. Use word_tokenize and select words starting with 'P'

Solution

1. Step 1: Understand ne_chunk output structure

   Named entities are subtrees labeled with entity types like 'PERSON', so we must traverse the tree to find these subtrees.

2. Step 2: Evaluate filtering methods

   pos_tag does not label entities, only parts of speech. Capital letters or starting with 'P' are unreliable heuristics.

3. Final Answer:

   Traverse the tree and select subtrees with label 'PERSON' -> Option A

4. Quick Check:

   Filter PERSON by subtree label [OK]

Hint: Filter PERSON entities by subtree label in ne_chunk tree [OK]

Common Mistakes:

• Using pos_tag to find entities
• Filtering by capitalization only
• Selecting words by first letter