# The Power of Query Rewriting in NLP: A Deep Dive for Beginners

In the world of Natural Language Processing (NLP), understanding and manipulating search queries is crucial for enhancing search performance. One of the most powerful techniques in this domain is query rewriting. This blog provides an in-depth exploration of query rewriting, focusing on how it enhances search recall and precision. We'll use a single e-commerce example throughout to illustrate these concepts and include Python code snippets to demonstrate their implementation.

## Why Do We Need Query Rewriting?

Query rewriting is essential because it helps search engines better represent the searcher's intent, thereby improving the quality of search results. This is particularly important in e-commerce, where precise and relevant search results can significantly impact the user experience and sales.

### Real-Life Use Case: E-Commerce

Imagine you're running an e-commerce site that sells various electronic gadgets. A user searches for "wireless earbuds." The challenge is to ensure that the search engine retrieves all relevant products, even if the user's query doesn't exactly match the product descriptions in the database. Query rewriting techniques like query expansion and query relaxation can help achieve this goal.

## Increasing Recall

Recall refers to the ability of a search system to retrieve all relevant documents. Increasing recall is crucial when the initial query returns few or no results.

### Query Expansion

Query expansion broadens the search query by adding additional tokens or phrases. These additional tokens may be synonyms, abbreviations, or terms obtained through stemming and spelling correction.

**Example:**

* Original Query: `wireless earbuds`
* Expanded Query: `(wireless OR bluetooth) AND (earbuds OR earphones)`
* This expansion ensures that products described as "Bluetooth earphones" are also retrieved.

**Python Code Example:**

from nltk.corpus import wordnet

def get_synonyms(word):
    synonyms = set()
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonyms.add(lemma.name())
    return list(synonyms)

query = "wireless earbuds"
expanded_query = []

for word in query.split():
    synonyms = get_synonyms(word)
    synonyms.append(word)
    expanded_query.append(f"({' OR '.join(synonyms)})")

expanded_query_str = " AND ".join(expanded_query)
print(expanded_query_str)

### Query Relaxation

Query relaxation increases recall by removing or optionalizing certain tokens that may not be essential for relevance.

**Example:**

* Original Query: `noise-cancelling wireless earbuds`
* Relaxed Query: `wireless earbuds`
* This relaxation allows the search engine to return results for "wireless earbuds" even if they don't explicitly mention "noise-cancelling."

**Python Code Example:**

query = "noise-cancelling wireless earbuds"
tokens = query.split()

# Naive relaxation strategy: remove one token
relaxed_queries = [' '.join(tokens[:i] + tokens[i+1:]) for i in range(len(tokens))]

print(relaxed_queries)

## Increasing Precision

Precision refers to the ability of a search system to retrieve only the relevant documents. Increasing precision is vital when the initial query returns a large set of heterogeneous results.

### Query Segmentation

Query segmentation involves treating multiple tokens as a single semantic unit. This can significantly improve precision by avoiding irrelevant matches.

**Example:**

* Original Query: `noise cancelling wireless earbuds`
* Segmented Query: `"noise cancelling" wireless earbuds`
* This segmentation ensures that the search engine looks for products that match the exact phrase "noise cancelling."

**Python Code Example:**

import re

query = "noise cancelling wireless earbuds"
segments = ['noise cancelling', 'wireless earbuds']

for segment in segments:
    query = re.sub(r'\b{}\b'.format(segment.replace(' ', '\s+')), f'"{segment}"', query)

print(query)

### Query Scoping

Query scoping restricts how different parts of the query match different parts of the documents. This can improve precision by associating query segments with specific document fields.

**Example:**

* Original Query: `Sony wireless earbuds`
* Scoped Query: `brand:Sony AND product:wireless earbuds`
* This scoping ensures that "Sony" matches the brand field, and "wireless earbuds" matches the product field.

**Python Code Example:**

query = "Sony wireless earbuds"
brand_field = "brand"
product_field = "product"

scoped_query = f"{brand_field}:Sony AND {product_field}:wireless earbuds"
print(scoped_query)

# Conclusion
Query rewriting is a powerful tool in NLP that enhances both recall and precision in search systems. By understanding and implementing techniques like query expansion, query relaxation, query segmentation, and query scoping, you can significantly improve the search experience on your e-commerce platform.

`Future posts will dive deeper into these techniques, providing more detailed algorithms and additional real-world examples. Stay tuned to master the art of query rewriting and take your search capabilities to the next level.`