NLP | Word Collocations

In natural language processing (NLP), a collocation is a sequence of words that co-occur more often than would be expected by chance. Examples of collocations are "strong tea", "fast car", or "make a decision". Collocations are useful for various NLP tasks such as text summarization, semantic analysis, and more.

Here's how you can identify word collocations using the Natural Language Toolkit (NLTK) in Python:

1. Install and Import Required Libraries

!pip install nltk import nltk from nltk.collocations import BigramCollocationFinder, TrigramCollocationFinder from nltk.metrics import BigramAssocMeasures, TrigramAssocMeasures from nltk.corpus import stopwords nltk.download('stopwords') 

2. Tokenize and Filter the Text

Before identifying collocations, you'll want to tokenize your text and filter out stopwords and punctuation.

# Sample text text = """ Natural language processing is a sub-field of artificial intelligence. It focuses on enabling machines to understand and respond to human language. """ # Tokenization words = nltk.word_tokenize(text) # Remove stopwords and punctuation filtered_words = [word for word in words if word not in stopwords.words('english') and word.isalnum()] 

3. Find Bigram Collocations

bigram_measures = BigramAssocMeasures() bigram_finder = BigramCollocationFinder.from_words(filtered_words) # Top 5 bigrams using Pointwise Mutual Information top_bigrams = bigram_finder.nbest(bigram_measures.pmi, 5) print(top_bigrams) 

4. Find Trigram Collocations

trigram_measures = TrigramAssocMeasures() trigram_finder = TrigramCollocationFinder.from_words(filtered_words) # Top 5 trigrams using Pointwise Mutual Information top_trigrams = trigram_finder.nbest(trigram_measures.pmi, 5) print(top_trigrams) 

In the examples above, Pointwise Mutual Information (PMI) is used to score collocations, but there are other metrics available, such as the chi-square test or likelihood ratio.

Keep in mind that extracting meaningful collocations often requires a larger corpus of text. Using just a few sentences, as in the provided example, might not yield particularly insightful results. Adjust the text and experiment with the techniques to get a deeper understanding of collocations in your specific dataset.

---

More Tags

flask embedded-tomcat-8 dependency-injection javascriptserializer stdout difference android-bottomsheetdialog pem angular-ngselect extract

More Programming Guides

• Passing Arrays as Arguments in C#
• Spring Data JPA - Delete Records From MySQL
• For loop in Java
• C++ Object Oriented Programming (OOPs)
• Provide documentation for Python functions
• How to convert DateTime to Unix timestamp in C#
• HTML object
• MySql Command Line Client Flashback Solution

Other Guides

• Android get Current UTC time
• How to cast an int to a string in C#?
• Read SQL Table into C# DataTable
• What is any character (including new line) pattern in regex?
• How to submit changes in LinqPad

More Programming Examples

• Scala: finding the difference between 2 lists
• python - How to efficiently iterate a pandas DataFrame and increment a NumPy array on these values?
• c# - How to make gridview column visible true or false dynamically?
• android - How to instruct Gradle to use Java from different location?
• Is it okay to call onClick on an anchor tag in ReactJS
• sql - How to cast bigint to timestamp with time zone in postgres in an update