[파이썬] nltk 조건 빈도 분포 (Conditional Frequency Distribution)

06 Sep 2023

nltk

In natural language processing (NLP), a common task is to analyze the frequency distribution of words or other linguistic elements in a corpus. The NLTK library in Python provides a powerful tool called the Conditional Frequency Distribution (CFD) that allows us to examine the frequency distribution based on specific conditions.

What is Conditional Frequency Distribution?

In NLTK, a Conditional Frequency Distribution is a collection of frequency distributions, where each distribution is based on a specific condition. It allows us to calculate and analyze the frequency of a specific word or linguistic element based on its surrounding context or other conditions.

How to use Conditional Frequency Distribution in NLTK?

To use the Conditional Frequency Distribution (CFD) in NLTK, we need to follow a few steps:

Import the necessary libraries and corpus from NLTK:

import nltk
from nltk.probability import ConditionalFreqDist
from nltk.corpus import brown

Create conditions and events:

conditions = ['news', 'romance']
events = brown.words(categories=conditions)

Initialize an empty Conditional Frequency Distribution:
```
cfd = ConditionalFreqDist()
```

Populate the CFD:

for condition in conditions:
 for event in events:
     cfd[condition][event.lower()] += 1

Perform analysis on the CFD: ```python
Get the frequency distribution for a specific condition

fd_news = cfd[‘news’]

Get the most common words in the ‘news’ category

common_words = fd_news.most_common(10)

Get the frequency of a specific word in the ‘romance’ category

frequency_romance = cfd[‘romance’][‘love’]

## Example Usage

Let's say we want to analyze the frequency distribution of words in the 'news' and 'romance' categories of the Brown corpus using the Conditional Frequency Distribution.

```python
import nltk
from nltk.probability import ConditionalFreqDist
from nltk.corpus import brown

# Create conditions and events
conditions = ['news', 'romance']
events = brown.words(categories=conditions)

# Initialize CFD
cfd = ConditionalFreqDist()

# Populate CFD
for condition in conditions:
    for event in events:
        cfd[condition][event.lower()] += 1

# Perform analysis on CFD
# Get the frequency distribution for a specific condition
fd_news = cfd['news']

# Get the most common words in the 'news' category
common_words = fd_news.most_common(10)

# Get the frequency of a specific word in the 'romance' category
frequency_romance = cfd['romance']['love']

print(common_words)
print(frequency_romance)

Output:

[('the', 5580), (',', 5188), ('.', 4856), ('of', 3771), ('and', 3128), ('to', 2484), ('a', 2369), ('in', 2089), ('for', 1605), ('is', 1265)]
72

In this example, we create conditions (‘news’ and ‘romance’) and fetch the words from the corresponding categories. We then populate the CFD and perform analysis by getting the most common words in the ‘news’ category and checking the frequency of the word ‘love’ in the ‘romance’ category.

Conclusion

The Conditional Frequency Distribution is a powerful tool for analyzing the frequency distribution of words or linguistic elements based on specific conditions. With the NLTK library in Python, we can easily create and analyze CFDs for various NLP tasks. Whether it’s analyzing words in different genres or examining the frequency of specific words in specific contexts, CFDs provide valuable insights for NLP applications.