To use NLTK (Natural Language Toolkit) in Databricks, follow these steps:

Installation

  1. Open a new cell in your Databricks notebook.
  2. Install NLTK by running the following command:
python
%pip install nltk

This will install the NLTK library in your notebook environment.

Importing NLTK

After installation, you can import NLTK in your notebook:

python
import nltk

You can also import specific modules as needed:

python
from nltk.tokenize import word_tokenize

Downloading NLTK Data

Some NLTK functionalities require additional data or resources. To download these, use the nltk.download() function:

python
nltk.download('punkt')

Replace ‘punkt’ with the name of the specific resource you need.

Using NLTK in Your Code

Now you can use NLTK functions in your Databricks notebook. For example, to tokenize a sentence:

python
text = "NLTK is a powerful library for natural language processing."
tokens = word_tokenize(text)
print(tokens)

Handling Multi-Node Clusters

If you’re using a multi-node Databricks cluster, you might encounter issues with NLTK data availability across nodes. To resolve this:

  1. Create an init script that downloads NLTK data to a shared location accessible by all nodes.
  2. Add the script to your cluster configuration under Advanced Options > Init Scripts.

Example: Named Entity Recognition

Here’s a simple example of using NLTK for named entity recognition in Databricks:

python
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')
nltk.download('words')
text = “John works at Google in New York.”
tokens = nltk.word_tokenize(text)
pos_tags = nltk.pos_tag(tokens)
named_entities = nltk.ne_chunk(pos_tags)
print(named_entities)

This code will tokenize the text, perform part-of-speech tagging, and identify named entities.

By following these steps, you can effectively use NLTK for various natural language processing tasks in your Databricks environment.