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Spelling Correction in IR Systems

The document outlines a practical exercise for developing a spelling correction module using edit distance algorithms, specifically the Levenshtein distance. It includes Python code for implementing the algorithm and integrating it into an information retrieval system to suggest corrections for misspelled words. The example demonstrates the functionality by correcting user input and retrieving information based on corrected queries.

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VIGHNESH RAJEEVAN
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20 views2 pages

Spelling Correction in IR Systems

The document outlines a practical exercise for developing a spelling correction module using edit distance algorithms, specifically the Levenshtein distance. It includes Python code for implementing the algorithm and integrating it into an information retrieval system to suggest corrections for misspelled words. The example demonstrates the functionality by correcting user input and retrieving information based on corrected queries.

Uploaded by

VIGHNESH RAJEEVAN
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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TYBSc(CS) USCSP6032: Information Retrieval Roll No: 3

Practical – 3

Aim: Spelling Correction in IR Systems


• Develop a spelling correction module using edit distance algorithms.
• Integrate the spelling correction module into an information retrieval system.

Solution:

Step 1: Type the following code and run the python file:

def levenshtein_dist(str1, str2):


len_str1 = len(str1) + 1
len_str2 = len(str2) + 1
matrix = [[0 for _ in range(len_str2)] for _ in range(len_str1)]
for i in range(len_str1):
matrix[i][0] = i
for j in range(len_str2):
matrix[0][j] = j
for i in range(1, len_str1):
for j in range(len_str2):
cost = 0 if str1[i-1] == str2[j-1] else 1
matrix[i][j] = min(
matrix[i-1][j] + 1,
matrix[i][j-1] + 1,
matrix[i-1][j-1] + cost
)
return matrix[len_str1 - 1][len_str2 - 1]
def suggest_correction(word, word_list):

Department of CS (2024-25) VKKM 1


TYBSc(CS) USCSP6032: Information Retrieval Roll No: 3

dist = [(w, levenshtein_dist(word, w)) for w in word_list]


dist.sort(key = lambda x: x[1])
return dist[0][0]
def retrieve_info(query, dictionary):
query_w = query.split()
corrected_words = [suggest_correction(x, dictionary) for x in query_w]
correct_query = " ".join(corrected_words)
print(f"Retrieving information for '{query}': {correct_query}")
input_word = "helo"
dictionary = ["hello", "world", "python", "spell", "correct", "algorithm"]
suggested_correction = suggest_correction(input_word, dictionary)
print(f"Suggested correction for '{input_word}': {suggested_correction}")
user_query = "seplinng corecton algothm"
dictionary = ["spelling", "correction", "algorithm", "information", "retrieval"]
retrieve_info(user_query, dictionary)

Output:

Department of CS (2024-25) VKKM 2

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