https://github.com/srvCodes/mt-dma
https://github.com/srvCodes/mt-dma
Last synced: 3 days ago
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- Host: GitHub
- URL: https://github.com/srvCodes/mt-dma
- Owner: srvCodes
- Created: 2018-11-17T10:44:52.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-11-17T11:31:47.000Z (over 7 years ago)
- Last Synced: 2024-04-28T15:34:48.850Z (about 2 years ago)
- Language: Python
- Size: 5.57 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-nlp - Multi-Task Deep Morphological Analyzer - deep morphological parser for Hindi and Urdu. (NLP per Language / Libraries and Tooling)
README
# Multi-Task Deep Morphological Analyzer
This repo contains the code for our paper entitled **Multi Task Deep Morphological Analyzer : Context Aware Neural Joint Morphological Tagging and Lemma Prediction**. The Web API service is accessible [here](http://35.154.251.44/).
A sample analysis:
## Experiements
Both the directories follow the organization:
1. [preProcessing](https://github.com/Saurav0074/mt-dma/tree/master/hindi/preProcessing) contains the code for dataset parsing. Datasets can be downloaded from the website of [Universal Dependencies](http://universaldependencies.org/).
2. [dataInfo](https://github.com/Saurav0074/mt-dma/tree/master/hindi/dataInfo) contains details on data set statistics.
3. [Models](https://github.com/Saurav0074/mt-dma/tree/master/hindi/models) for all experiments:
- [multiTask_with_context4.py](https://github.com/Saurav0074/mt-dma/blob/master/hindi/models/multiTask_with_context4.py) hosts the fully BiLSTM model for a CW of 4 words.
- [multiTask_with_attention.py](https://github.com/Saurav0074/mt-dma/blob/master/hindi/models/multiTask_with_attention.py) hosts the character CNN-RNN based MT-DMA model, as reported in the paper.
- [onlyFeatures.py](https://github.com/Saurav0074/mt-dma/blob/master/hindi/models/onlyFeatures.py) and [onlyRoots.py](https://github.com/Saurav0074/mt-dma/blob/master/hindi/models/onlyRoots.py) contain the codes for individual learning.
4. [Code](https://github.com/Saurav0074/mt-dma/tree/master/hindi/featureOptimization)
for MOO based GA feature selection.
5. Code for post processing, visualization, BLEU, Levenshtein and word accuracy calculation can be found in [postProcessingAndVisualization](https://github.com/Saurav0074/mt-dma/tree/master/hindi/postProcessingAndVisualization).
5. [Outputs](https://github.com/Saurav0074/mt-dma/tree/master/hindi/outputs) on the HDTB and UDTB datasets.
6. Outputs for [t-SNE plots](https://github.com/Saurav0074/mt-dma/tree/master/hindi/tsnePlots), [GA graphs](https://github.com/Saurav0074/mt-dma/tree/master/hindi/gaGraphs), and [Precision-Recall curves](https://github.com/Saurav0074/mt-dma/tree/master/hindi/prCurves).
# MOO optimization
Cubic-spline interpolations for validation accuracies of population:
