- Gain a practical understanding of traditional and modern natural language processing techniques.
- Develop an intuition for knowledge graphs and ontologies.
- Familiarisation with basic text handling and processing such as lemmatisation, stemming, etc.
- Gain intuition towards word vectors and their applications in natural language processing.
- Develop an understanding of unsupervised learning using latent topic models
- Develop an understanding of supervised learning using modern tools such as PyTorch for sentiment analysis.
| AWST | AEST | Agenda |
|---|---|---|
| 07:30 - 07:45 | 09:30 - 09:45 | Q&A, Issues & Announcements |
| 07:45 - 09:15 | 09:45 - 11:15 | Session 1: Handling Text and Basic Text Processing |
| 09:15 - 09:30 | 11:15 - 11:30 | Morning Tea |
| 09:30 - 11:00 | 11:30 - 13:00 | Session 2: Word Embeddings |
| 11:00 - 11:45 | 13:00 - 13:45 | Lunch |
| 11:45 - 13:15 | 13:45 - 15:15 | Session 3: Unsupervised Learning |
| 13:15 - 13:30 | 15:15 - 15:30 | Afternoon Tea |
| 13:30 - 14:45 | 15:30 - 16:45 | Session 4: Supervised Learning |
| 14:45 - 15:00 | 16:45 - 17:00 | Closeout |
Additional information pertaining to chat based discussions and material within the workshop:
- Centre for Transforming Maintenance Through Data Science (CTMTDS): https://www.maintenance.org.au/
- CTMTDS - Theme 1 Support the Maintainer (Wei & Tyler; NLP): https://www.maintenance.org.au/category/rt1
- Industrial Ontologies - Maintenance Working Group: https://www.industrialontologies.org/?page_id=92
- Aquila exploratory data analysis tool: http://agent.csse.uwa.edu.au/aquila
- Spacy - Industrial Strength Natural Language Processing: https://spacy.io/
- Gensim - Topic Modelling for Humans: https://radimrehurek.com/gensim/
- NLTK - Natural Language Tool Kit: https://www.nltk.org/
- Interactive word2vec (embedding) visualisation tool: https://ronxin.github.io/wevi/
- PyTorch - Binary Cross Entropy Loss (BCELoss): https://pytorch.org/docs/stable/nn.html#bceloss
- PyTorch - Recurrent Neural Network (RNN) module: https://pytorch.org/docs/stable/nn.html#rnn
- CUDA framework for GPU training: https://developer.nvidia.com/cudnn
- CUDA supported GPUs: https://developer.nvidia.com/cuda-gpus
- Automatic Summarization (NLP/NLG): https://en.wikipedia.org/wiki/Automatic_summarization
- Industrial Ontologies - Maintenance Working Group: https://www.industrialontologies.org/?page_id=92
- Example of embeddings drawing powerful insights into COVID19 research: https://www.kaggle.com/tarunpaparaju/covid-19-dataset-gaining-actionable-insights