This is a space for the labs of TDT4310, including the tasks and supplementary material.
Main points:
- Labs released every second Friday (see dates below)
- You get an initial week to work with (and possibly finish) the lab
- Lab session takes place. I will do my best to assist as many as I can. Note that this is an open session, where I encourage you to talk to your peers and discuss in groups.
- Deadline is 1 week after the lab session.
For any other questions that may not be suitable for the lab sessions, contact us through the emails provided on blackboard.
This year, the course curriculum is mostly based around the brand new book by Ekaterina Kochmar -- Getting Started with Natural Language Processing. For purchasing instructions (+ discount), visit the course page on Blackboard.
We'll also use the NLTK library. As usual, more information on textbooks and other resources can be found on blackboard.
All exercises/labs will follow this format:
- Questions (provided as pdf/markdown) relevant to the current lab (e.g. LAB_1.pdf)
- server code that you will implement to improve the keyboard, found under
labs/your_implementations/lab_n.py.
It is highly recommended to complete the initial questions before moving on to the implementation.
❗️Delivery❗️ Delivery guidelines are found in each lab. In short, submit a zip file with your NTNU username, containing your answers to the exercises as a document, in addition to a python file with your completed implementation of the lab.
For the exercises, keep your answers short. These questions are meant to guide you towards an understanding of the topics, so that you can focus on the implementation.
Here's a brief overview of the labs
| Lab | Link | Published | Deadline | Topic | Smart keyboard features | Libraries | Chapters |
|---|---|---|---|---|---|---|---|
| 1 | Lab1 | Jan. 10 | Jan. 30 | Tokenization, introduction to word vectors and language modeling | Next-word prediction | NLTK | 2, 3 |
| 2 | Lab2 | Jan. 27 | Feb. 13 | Part-of-speech tagging, stemming/lemmatization, TF-IDF | Utilize part-of-speech for predictions | NLTK, spaCy | 4, 5, 6 |
| 3 | Lab3 | Feb. 10 | Feb. 27 | Wordnet and SentiWordNet, dependency parsing, POS chunking | Sentiment-based and multi-word predictions | spaCy, Scikit-learn | 7, 8 |
| 4 | Lab4 | Feb. 24 | Mar. 13 | Unsupervised topic modeling and named entities | - | Gensim | 9, 10, 11 |
Updates:
- [January 27]: All deadlines have been extended by 3 days to reflect the extension of Lab 1.
Throughout the labs, alongside a few questions and simple tasks, you will be implementing a smart keyboard, mostly from scratch. I have prepared a full-stack application, where your task is to build a system that suggests the next word(s). The frontend application (which you do not need to modify) is built using React.
The backend, which you will be working with, is built using Flask. This course assumes fairly good knowledge of programming in general, and you are expected to be able to debug potential issues with the system yourself. I will, of course, do my best to aid you in this process. A screenshot of the application is shown below.
You are required to run a few commands to get the system up and running, both for the frontend and the backend.
I suggest you download all packages in NLTK so you can experiment freely without caring about downloading separate packages later on. Data is stored in nltk_data in your user folder. The total size is around 3 GB. If you're low on space, look up how to download the packages you require.
cd labspip install -r requirements.txtpython -m nltk.downloader allpython server.py
You can always experiment with the output of your implementations before testing it in the server. The server is made mostly to be able to access the predictive keyboard in a more intuitive manner! The testing can be done by running the jupyter notebook found here: labs/notebooks/lab1.ipynb
The frontend application runs by doing the following:
- install node, which includes the
npmcli: https://nodejs.org/en/download/ cd labs_frontendnpm installnpm start
If npm install fails, you may have issues with your node installation or version. An alternative way to run this is as follows:
cd labs_frontendnpm install -g serveserve -s build(this will run the precompiled application)
A very simplified flowchart, that may help you grasp the overall functionality:
The labs are not graded, but pass/fail. You need to pass all labs to be eligible for the exam. The criteria will be explained further on blackboard and in lectures.
Try to keep questions regarding labs to the lab session. Other questions may be posted on Blackboard.
Some info on the libraries used:
- NLTK has an extensive collection of both explanations and sample usages:
- Spacy: https://spacy.io/api
- Scikit-learn: https://scikit-learn.org/stable/modules/classes.html
- Gensim: https://radimrehurek.com/gensim/apiref.html
Jupyter notebook is a beneficial tool for working with models and anything else in python. Install it:
pip install jupyterlab
After you have installed all the dependencies, you can run a notebook server:
jupyter-labThis will start a slightly fancier version of jupyter notebook. It is a single-page application that allows you to navigate, edit, and run python and jupyter notebook files from your browser. By default, the server should be exposed at localhost port 8888. If you're running this command from your laptop or desktop computer, it should automatically open in your default web browser. If not, try manually navigating to localhost:8888 in your web browser (or copy the entire URL from the logging output in the shell you ran the command). If it asks for a password or token, this can also be found in the shell output.
From here, you can start running and editing the files in the project. If you need more help with the interface, there are several guides online.