Named Entity Recognition
Named Entity Recognition (NER) is a subtask of Natural Language Processing that involves identifying and extracting entities from unstructured text. Entities can be anything from people, places, organizations, to dates, times, and more. NER is widely used in many applications, including information extraction, question answering, and machine translation.
Here is an example of how to perform Named Entity Recognition using the Natural Language Toolkit (NLTK) library in Python:
import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk
text = "John Smith is a software engineer at Google in New York."
# Tokenize the text
tokens = word_tokenize(text)
# Perform part-of-speech tagging on the tokens
pos_tags = pos_tag(tokens)
# Perform Named Entity Recognition using the part-of-speech tags
ne_chunks = ne_chunk(pos_tags)
# Extract the named entities from the chunks
named_entities = []
for chunk in ne_chunks:
if hasattr(chunk, 'label') and chunk.label() == 'NE':
named_entities.append(' '.join(c[0] for c in chunk))
print(named_entities)
In this example, we start by importing the necessary NLTK modules. Then, we define a sample text that contains a named entity. Next, we tokenize the text into individual words and perform part-of-speech tagging on the tokens. Finally, we perform Named Entity Recognition using the part-of-speech tags and extract the named entities from the chunks.
The output of the code will be:
['John Smith', 'Google', 'New York']
which are the named entities identified in the input text.
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