Named entity recognition in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for named entity recognition.

NLP

import [1]

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

Asklearn

Bpandas

Cmatplotlib

Dnltk

Attempts:

3 left

💡 Hint

Common Mistakes

Importing pandas or matplotlib which are not used for NLP.

Using sklearn which is mainly for machine learning but not specifically for NER.

The nltk library is commonly used for natural language processing tasks including named entity recognition.

2fill in blank

medium

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

NLP

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

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Aword_tokenize

Bsent_tokenize

Cpos_tag

Dne_chunk

Attempts:

3 left

💡 Hint

Common Mistakes

Using sent_tokenize which splits text into sentences, not words.

Using pos_tag or ne_chunk before tokenizing.

word_tokenize splits the sentence into words, which is the first step before tagging or chunking.

3fill in blank

hard

Fix the error in the code to perform named entity recognition on tokenized text.

NLP

from nltk import pos_tag, ne_chunk

tokens = ['Apple', 'is', 'looking', 'at', 'buying', 'U.K.', 'startup', 'for', '$', '1', 'billion']
pos_tags = [1](tokens)
named_entities = ne_chunk(pos_tags)

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

Aword_tokenize

Bpos_tag

Csent_tokenize

Dne_chunk

Attempts:

3 left

💡 Hint

Common Mistakes

Using word_tokenize on already tokenized list.

Using ne_chunk before pos_tag.

pos_tag is used to assign part-of-speech tags to tokens before named entity chunking.

4fill in blank

hard

Fill both blanks to extract named entities as a list of tuples (entity, type).

NLP

entities = []
for subtree in named_entities.[1]():
    if hasattr(subtree, '[2]'):
        entity_name = ' '.join([token for token, pos in subtree.leaves()])
        entity_type = subtree.label()
        entities.append((entity_name, entity_type))

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Asubtrees

Bchildren

Clabel

Dleaves

Attempts:

3 left

💡 Hint

Common Mistakes

Using children() which returns immediate children but not all subtrees.

Checking for leaves attribute instead of label.

We use subtrees() to iterate over chunks and check if they have a label attribute to identify named entities.

5fill in blank

hard

Fill all three blanks to train a simple NER model using spaCy.

NLP

import spacy
from spacy.training import Example

nlp = spacy.blank('en')
ner = nlp.add_pipe('[1]')

TRAIN_DATA = [
    ("Google is a tech company", {"entities": [(0, 6, '[2]')]}),
    ("I live in New York", {"entities": [(10, 18, '[3]')]})
]

optimizer = nlp.begin_training()
for itn in range(10):
    for text, annotations in TRAIN_DATA:
        doc = nlp.make_doc(text)
        example = Example.from_dict(doc, annotations)
        nlp.update([example], sgd=optimizer)

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Aner

BORG

CGPE

Dtextcat

Attempts:

3 left

💡 Hint

Common Mistakes

Adding 'textcat' pipe which is for text classification, not NER.

Using wrong entity labels like 'LOC' instead of 'GPE'.

We add the ner pipe to the model, and label entities as ORG (organization) and GPE (geopolitical entity).

Practice

1. What is the main goal of Named Entity Recognition (NER) in natural language processing?

easy

A. To find and label names of people, places, and dates in text

B. To translate text from one language to another

C. To summarize long documents into short paragraphs

D. To generate new text based on input

Solution

1. Step 1: Understand NER purpose

   NER is designed to identify and label specific types of information like names, places, and dates in text.

2. Step 2: Compare with other NLP tasks

   Translation, summarization, and text generation are different tasks unrelated to labeling entities.

3. Final Answer:

   To find and label names of people, places, and dates in text -> Option A

4. Quick Check:

   NER = Labeling names in text [OK]

Hint: NER finds names and dates in text, not translations or summaries [OK]

Common Mistakes:

• Confusing NER with translation or summarization
• Thinking NER generates new text
• Believing NER only finds keywords, not entities

2. Which of the following is the correct way to import a Named Entity Recognition pipeline using Hugging Face Transformers in Python?

easy

A. import pipeline from transformers; ner = pipeline('named_entity')

B. from transformers import pipeline; ner = pipeline('ner')

C. from transformers import ner_pipeline; ner = ner_pipeline()

D. import ner from transformers; ner = pipeline('ner')

Solution

1. Step 1: Recall correct import syntax

   The Hugging Face library uses 'from transformers import pipeline' to import the pipeline function.

2. Step 2: Check pipeline usage for NER

   Calling pipeline('ner') creates a named entity recognition pipeline correctly.

3. Final Answer:

   from transformers import pipeline; ner = pipeline('ner') -> Option B

4. Quick Check:

   Correct import and pipeline call = from transformers import pipeline; ner = pipeline('ner') [OK]

Hint: Use 'from transformers import pipeline' and call pipeline('ner') [OK]

Common Mistakes:

• Using incorrect import syntax
• Calling pipeline with wrong task name
• Trying to import non-existent functions

3. Given the following Python code using Hugging Face Transformers NER pipeline:

from transformers import pipeline
ner = pipeline('ner')
text = "Barack Obama was born in Hawaii on August 4, 1961."
results = ner(text)
print(results)

What will be the output type of results?

medium

A. A single string with all entities concatenated

B. A dictionary with entity counts

C. A list of dictionaries with entity details

D. An integer representing number of entities

Solution

1. Step 1: Understand pipeline output format

   The NER pipeline returns a list where each item is a dictionary describing an entity found in the text.

2. Step 2: Check example output structure

   Each dictionary contains keys like 'entity', 'score', 'index', and 'word' describing the entity.

3. Final Answer:

   A list of dictionaries with entity details -> Option C

4. Quick Check:

   NER output = list of entity dictionaries [OK]

Hint: NER pipeline returns list of dicts, not strings or counts [OK]

Common Mistakes:

• Expecting a single string output
• Thinking output is a dictionary summary
• Assuming output is just a count number

4. Consider this code snippet for NER using Hugging Face Transformers:

from transformers import pipeline
ner = pipeline('ner')
text = "Apple is looking at buying U.K. startup for $1 billion"
results = ner(text, grouped_entities=True)
print(results)

What is the likely error or issue here?

medium

A. The text input must be a list, not a string

B. The pipeline call is missing the model parameter

C. There is no error; code runs correctly

D. The argument 'grouped_entities' is invalid and causes a TypeError

Solution

1. Step 1: Check pipeline argument validity

   The 'grouped_entities' argument is not supported in the current pipeline call and will raise a TypeError.

2. Step 2: Confirm correct usage

   To group entities, the argument should be 'aggregation_strategy' with values like 'simple', not 'grouped_entities'.

3. Final Answer:

   The argument 'grouped_entities' is invalid and causes a TypeError -> Option D

4. Quick Check:

   Invalid argument causes error = The argument 'grouped_entities' is invalid and causes a TypeError [OK]

Hint: Check pipeline argument names carefully; 'grouped_entities' is wrong [OK]

Common Mistakes:

• Using unsupported argument names
• Assuming text input must be a list
• Thinking missing model parameter causes error

5. You want to extract all person names and locations from a news article using NER. Which approach best ensures you only get these two entity types from the pipeline output?

hard

A. Filter the NER results by checking if the entity label is 'PER' or 'LOC'

B. Use the pipeline with task='ner' and no filtering

C. Manually search the text for capitalized words

D. Train a new model only on person and location data

Solution

1. Step 1: Understand NER output labels

   NER results include entity labels like 'PER' for person and 'LOC' for location.

2. Step 2: Filter results for desired entities

   Filtering the output by these labels extracts only person and location entities effectively.

3. Final Answer:

   Filter the NER results by checking if the entity label is 'PER' or 'LOC' -> Option A

4. Quick Check:

   Filter by labels 'PER' and 'LOC' to get persons and locations [OK]

Hint: Filter NER output by entity labels to get specific types [OK]

Common Mistakes:

• Not filtering and getting all entity types
• Trying manual text search instead of using labels
• Assuming retraining is needed for filtering