This repository contains python implementation for calculating Longest Common Subsequences (LSC) between tokenized URDU sentences.
I am leaving this open source on the honor system. My implementation works for the preprocessed urdu data (1 sentence per line) as I used "\n" as my delimiter while extracting sentences. I was having issues with lengths of sentences while trying with "۔" delimiter. If you find a fix, feel free to contribute. Thanks.
The longest common subsequence (LCS) is defined as the longest subsequence that is common to all the given sequences, provided that the elements of the subsequence are not required to occupy consecutive positions within the original sequences. This repository contains the notebook and scripted implementation of calculating LCS by reading the raw urdu ttext file and showing the final LCS matrix on the console. The script use a naive and recurrent implementation of LCS Algorithm.
The input text file and the LCS output is shown below:
- SpaCy
- Pandas
- ur_model
- Clone the repository
- Point the current directory to cloned repository and install "ur_model" as follow:
pip install ur_model-0.0.0.tar.gz - You are all set to run the lcs.py