Clone the repo.
Create a python virtual environment using venv or virtualenv.
run pip install -r requirements.txt to download required python libraries.
The project uses spaCy for some tasks to install spaCy follow steps given here
After installing spacy run python -m spacy download en_core_web_lg to install the dataset for spaCy.
task1\Q1.ipynb is the code when Restrictions are there all other code files are named properly.
precog-task-report folder contains .tex and .bib codes along with figures for report.
For detailed explanation refer. to report.
Paper reading Task slides are availabel in precog-task-report/Paper-reading-task-ppt presentation is made in Reveal.js
Slides can be accessed online here

Task-1

Aim:

Implementing pseudo-unsupervised model to check Sentence similarity

If we just convert words to vectors and then find the cosine similarity between them as an idea to find the similarity that would give false results. For example: if I just use tf_idf vectors and apply cosine similarity then the result would be same for each sentence.
Also in Word2Vec approach we see that for each word it uses words before and after that word to make context. So if some words are randomly written and we train our model on that than we would find that the output given will be very wrong.
Also we observe that most of the python libraries like spaCy, nltk are not able to predict the similarity score if those words were not present in the training dataset. nltk and spaCy doesn't use supervised learining but rather goes with pseudo-unsupervised/ unsupervised learing for predicting similarity.

Given labeled data-set splitted into training, testing, dev. we have to predict the Phrase and sentence similarity.

Altough the labelled data was given. But i've used unsupervided learning in this task.
I'm using the frequency of how many pairs have similarity in the training data as my threshold frequency. And calculating if test pairs have similarity score $>$ threshold or not.
Doing this would always give the similarity score to be 1. Because in the dataset each pair sentences are just paraphrased.

for example: consider these 2 sentences{\cite{paws2019naacl}} to follow the approch mentioned above.
- In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland.
- In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
In the above example all the things are same except the highlighted part. As discussed in the Task1 that for a given word word2vec looks at words before and after to make context of the words. Due to this reason the model can't tell the difference between the two sentences.
Now consider this example:
- This was a series of nested angular standards , so that measurements in azimuth and elevation could be done directly in polar coordinates relative to the ecliptic .
- This was a series of nested polar scales , so that measurements in azimuth and elevation could be performed directly in angular coordinates relative to the ecliptic .
Over here the difference is pretty big but the model wan't able to identify the dissimilarity in these sentences. So to tackle situations like this I also add a check to compare Parts of Speech for both Sentences.