https://github.com/cltl-students/siti_nurhalimah_thesis_2023

https://github.com/cltl-students/siti_nurhalimah_thesis_2023

Repository

https://github.com/cltl-students/Siti_Nurhalimah_Thesis_2023/blob/main/

# Description
This repository is a part of the thesis project for MA Linguistics (Human Language Technology). This project focuses on the cleaning up of [Wordnet Bahasa](https://compling.upol.cz/ntumc/cgi-bin/wn-gridx.cgi?gridmode=gridx) by comparing automatically aligned dictionary data ([OPUS](https://opus.nlpl.eu/)) with hand-curated dictionary data ([Wiktionary](https://kaikki.org/dictionary/English/index.html)) using the multilingual sense intersection (MSI) methodology. This repository contains tools and scripts for performing sense alignment and evaluation using various datasets and algorithms. The repository is organized into different subfolders and contains a variety of Jupyter notebooks and Python scripts to facilitate the process.

## Special Note:
Due to limited storage space on GitHub, all the complete repository along with its data can be downloaded from this [Google Drive](https://drive.google.com/file/d/1_3RWldGglV-cLoL1QH20uxe-fBqEVtY4/view?usp=sharing) as well.

JSON file for Wiktionary extraction compiled by Ylonen (2022) can be download through [kaikki.org](https://kaikki.org/dictionary/English/index.html) website. Navigate to the **Download** section. Once the file is downloaded, store the file into [data](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wiktionary/data) and rename the file as _kaikki.org-dictionary-English.json_.

## Table of Contents
- [Opus Folder](#opus-folder)
- [Wiktionary Folder](#wiktionary-folder)
- [wn-msa-all Folder](#wn-msa-all-folder)
- [requirement.txt](#requirementtxt)
- [Usage](#usage)
- [Contact](#contact)
- [License](#license)

## opus Folder

The [`opus`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus) folder contains data related to the OPUS corpus, a parallel data collection. It is organized as follows:

### Subfolders:

- [data](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/data) stores all the files of the original parallel data from SourceForge.
- [predictions_results](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/predictions_results) stores prediction results of the system's sense alignment.
- [raw_data_bible](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/raw_data_bible) contains the Bible (Uedin) corpus.
- [raw_data_opensubtitle](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/raw_data_opensubtitle) contains the OpenSubtitles corpus.
- [raw_data_qed](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/raw_data_qed) contains the QED corpus.
- [raw_data_tanzil](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/raw_data_tanzil) stores the Tanzil corpus.

### Jupyter Notebooks and scripts:

- [1. opus_extraction.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/1.%20opus_extraction.ipynb) parses data from the four different corpora.
- [2. intersection_algorithm_opus.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/2.%20intersection_algorithm_opus.ipynb) maps senses using the intersection algorithm on the OPUS data.
- [3. opus_algorithm_based_conditions.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/3.%20opus_algorithm_based_conditions.ipynb) runs the mapped senses based on five conditions.
- [4. opus_token_count.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/4.%20opus_token_count.ipynb) counts the number of tokens in the OPUS corpus for data analysis.
- [5. opus_languages_intersection_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/5.%20opus_languages_intersection_analysis.ipynb) counts the number of language intersections in the OPUS corpus.
- [6. data_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/6.%20data_analysis.ipynb) counts the number of senses suggested by one or two languages in the development set for data analysis.
- [7. error_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/7.%20error_analysis.ipynb) counts senses suggested by certain languages based on evaluation set results for error analysis.
- [8. evaluation_set.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/8.%20evaluation_set.ipynb) generates matching between OPUS and the evaluation set and runs the intersection algorithm using the best condition (condition 5).
- [classification_report_generator.py](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/opus/classification_report_generator.py) generates a classification report in the [`predictions_results`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/opus/predictions_results) folder.

## wiktionary Folder

The [`wiktionary`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wiktionary) folder contains data related to the Wiktionary dataset. It is structured as follows:

### Subfolders:

- [data](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wiktionary/data): This directory stores all the files of the original parallel data from SourceForge.
- [predictions_results](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wiktionary/predictions_results): This is where the prediction results of the system's sense alignment are stored.

### Jupyter Notebooks and scripts:

- [1. wiktionary_extraction.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/1.%20wiktionary_extraction.ipynb) parses data from the Wiktionary JSON file downloaded from [kaikki.org](https://kaikki.org/dictionary/English/index.html).
- [2. intersection_algorithm_wiktionary.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/2.%20intersection_algorithm_wiktionary.ipynb) maps senses using the intersection algorithm on the Wiktionary data.
- [3. wiktionary_algorithm_based_conditions.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/3.%20wiktionary_algorithm_based_conditions.ipynb) runs the mapped senses based on five conditions.
- [4. wiktionary_token_count.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/4.%20wiktionary_token_count.ipynb) counts the number of tokens in the Wiktionary dataset for data analysis.
- [5. wiktionary_languages_intersection_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/5.%20wiktionary_languages_intersection_analysis.ipynb) counts the number of language intersections in the Wiktionary dataset.
- [6. POS_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/6.%20POS_analysis.ipynb) counts the number of parts of speech (POS) in Wiktionary for data analysis.
- [7. data_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/7.%20data_analysis.ipynb) counts the number of senses suggested by one or two languages in the development set for data analysis.
- [8. evaluation_set.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/8.%20evaluation_set.ipynb) generates matching between Wiktionary and the evaluation set and runs the intersection algorithm using the best condition (condition 5).
- [9. error_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/9.%20error_analysis.ipynb) counts senses suggested by certain languages based on evaluation set results for error analysis.
- [classification_report_generator.py](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wiktionary/classification_report_generator.py) generates a classification report in the [`predictions_results`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wiktionary/predictions_results) folder.

## wn-msa-all Folder

The [`wn-msa-all`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wn-msa-all) folder contains data related to the [main file](https://sourceforge.net/p/wn-msa/tab/HEAD/tree/trunk/) and additional data from Wordnet Bahasa maintainers. It is structured as follows:

### Subfolders:

- [data](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wn-msa-all/data) stores all the files of the original parallel data from SourceForge.
- [predictions_results](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wn-msa-all/predictions_results) stores prediction results of the system's sense alignment.

### Jupyter Notebooks and scripts:

- [1. development_evaluation_sets.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/1.%20development_evaluation_sets.ipynb) builds development and evaluation sets.
- [2. goodness_labels_extraction.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/2.%20goodness_labels_extraction.ipynb) extracts data from the [main data](https://sourceforge.net/p/wn-msa/tab/HEAD/tree/trunk/) with labels B and I, extracting goodness labels to the development and evaluation sets.
- [3. combined_datasets_on_eval.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/3.%20combined_datasets_on_eval.ipynb) combines both Wiktionary and OPUS data and runs the combined dataset on the evaluation set using the best condition. Generates a classification report.
- [4. wn-msa_error_analysis.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/4.%20wn-msa_error_analysis.ipynb) suggests senses for the [main data](https://sourceforge.net/p/wn-msa/tab/HEAD/tree/trunk/) using Wiktionary and OPUS data respectively for the final error analysis step. Takes 150 random samples from each data source.
- [5. error_analysis_hand-checked.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/5.%20error_analysis_hand-checked.ipynb) obtains glosses of the 150 random samples and generates accuracy scores for both Wiktionary and OPUS for the last step of error analysis.
- [senses.ipynb](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/senses.ipynb) checks senses, gloss, hypernym and hyponyms of a synset in WordNet for the purpose of further data anlysis.
- [classification_report_generator.py](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/wn-msa-all/classification_report_generator.py) generates a classification report in the [`predictions_results`](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/tree/main/wn-msa-all/predictions_results) folder.

## [requirement.txt](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/requirements.txt)
This file contains all the information about the packages needed to run the code. 

# Usage
To use this repository, first clone or download it to your local machine. Then, navigate to the respective folders based on your requirements. Each folder contains notebooks or scripts for specific tasks related to data analysis and intersection algorithms. Follow the instructions provided in the notebooks or scripts to execute the necessary operations.

# Contact
If you have any questions or encounter any issues, please feel free to reach out to the project owner at s.nurhalimah@student.vu.nl.

# [License.txt](https://github.com/iamima188/Siti_Nurhalimah_Thesis_2023/blob/main/LICENSE.txt)
Licence of the repository.

## References
H. B. M. Noor, S. Sapuan, and F. Bond. [Creating the open Wordnet Bahasa](https://aclanthology.org/Y11-1027). In Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation, pages 255264, Singapore, Dec. 2011. Institute of Digital Enhancement of Cog- nitive Processing, Waseda University.

Bond and G. Bonansinga. Exploring Cross-Lingual Sense Mapping in a Multilingual Parallel Corpus, pages 5661. 01 2015. ISBN 9788899200008. doi: 10.4000/books.aaccademia.1321

Kratochvil and L. Morgado da Costa. [Abui Wordnet: Using a toolbox dictionary to develop a wordnet for a low-resource language](https://aclanthology.org/2022.fieldmatters-1.7/). In Proceedings of the first workshop on NLP applications to field linguistics, pages 5463, Gyeongju, Republic of Korea, Oct. 2022. International Conference on Computational Linguistics.

Tiedemann. [Parallel data, tools and interfaces in opus](https://aclanthology.org/L12-1246/). In N. Calzolari, K. Choukri, T. Declerck, M. U. Dogan, B. Maegaard, J. Mariani, J. Odijk, and S. Piperidis, ed- itors, LREC, pages 22142218. European Language Resources Association (ELRA), 2012. ISBN 978-2-9517408-7-7.

Ylonen. Wiktextract: [Wiktionary as machine-readable structured data](https://aclanthology.org/2022.lrec-1.140). In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 13171325, Marseille, France, June 2022. European Language Resources Association.

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