- Run the command "python main.py" on Windows, or "python3 main.py" on UNIX based systems to run the model.
- Custom training and testing data can be added as a file. If there is no file, the code automatically uses the default file. Enter "NA" for the same.
- The results will be saved in the "Results" folder, under each language, named as "algorithm_results.txt", which contains the precision, recall, F1 score and the confusion matrix for the algorithm.
-
Notifications
You must be signed in to change notification settings - Fork 0
Conducted a comparative analysis of POS tagging algorithms—Viterbi, Posterior Decoding, Greedy, and Beam Search—within an HMM model across Hindi, English, Spanish, and Sanskrit, revealing key insights into each method’s performance, accuracy, and suitability for diverse linguistic contexts..
Kirti-Pant/-Optimizing-POS-Tagging-using-HMM-A-Comparison-of-Algorithmic-Approaches
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Conducted a comparative analysis of POS tagging algorithms—Viterbi, Posterior Decoding, Greedy, and Beam Search—within an HMM model across Hindi, English, Spanish, and Sanskrit, revealing key insights into each method’s performance, accuracy, and suitability for diverse linguistic contexts..
Resources
Stars
Watchers
Forks
Releases
No releases published