CS F469 Assignment 2

Plagiarism Detector in News Articles using LSH

Locality sensitive hashing (LSH) is a widely popular technique used in approximate nearest neighbor search. LSH uses an approximate search principle to get better performance while maintaining the quality of the results.
We have used a variation of LSH consisting of three major steps:

1. K-Shingling
2. Minhashing
3. Banded LSH function

At its core, the final LSH function allows us to segment and hash the same sample several times. And when we find that a pair of vectors has been hashed to the same value at least once, we tag them as candidate pairs — that is, potential matches.

In LSH we leverage these collisions of similar inputs to get our candidate pairs.

The first step of LSH is the creation of the signature matrix. Since incident matrix is too sparse and dimension heavy, we create a compact signature matrix using the minhashing technique.

get_hash_functions(hsh_cnt, mod) generates hsh_cnt random hash functions which will be used for minhashing. The generated hash functions are of the form $(ax + b)$ % $mod$

MinHashing Technique :

The signature matrix is created using the minhash technique where we apply all of the hash functions row-wise and update the minimum hash value in each document which has the current shingle.
The signature matrix is divided into b bands of r row each, for each band we hash its portion of the column in the corresponding bucket, Candidate column pairs are those that hash to the same bucket for at least 1 band.

The results are verified by actually checking if those documents actually have similarity >= threshold. The top 3 similar documents, number of candidate pairs along with statistics depicting performance of LSH (True Positive, True Negative, False Positive, False Negative) is returned.

Band Analysis of LSH

Consider $s$ is the similarity between two documents. The probability that the two documents do not collide in a band will be $1 - s_{}^{r}$ where $r$ is the number of rows in a band.
Hence the probability that the two documents collide in atleast one band (form a candidate pair) given the similarity $s$ will be $1 - (1 - s_{}^{r})_{}^{b}$ where $r$ is the number of rows in a band and $b$ is the number of bands.

For running code -

• Download the news article dataset from Kaggle

• Extract the dataset zip

• Run extract.ipynb code entirely once before running LSH.ipynb

• K is set in extract.ipynb change according to your convience

• Pickled Files are initially git ignored (so you will have to run extract.ipynb at least once)