Suppose if I have a small dataset containing some words and their tags/labels. The main task is to provide tags to other words(which are not in the dataset) based on their contextual relationship with the words already in the dataset.
Let's say, for example, my custom dataset includes
Soap --> label__(cleaning_agent) pencil--> label__(stationary_item) mobile--> label__(electronics) washingmachine--> label(electronics) and so on.
I want my program to be able to correctly predict the label of an unknown word, e.g.
washing powder to its correct category label__(cleaning_agent) radio to label__(electronics) etc.
Now the main problem is to find the relation between two words based on context, but I am not able to decide what can be the parameters for finding that.
I have tried a naive approach using datamuse API and fastText library.
Naive approach is as follows-> step 1-> find all the related words of the given word(let's say W) e.g. pencil using datamuse API. step 2-> combine them into a string(let's say S) with spaces in between them step 3-> use the label name, W, S as the training dataset for fastText.
NOTE: fastText requires label name, word, sentence(can be from news articles, blogs, Wikipedia etc ) as a context for that word.
RESULTS: fastText is not providing any reliable results. I am thinking of building a neural network kind of thing for this purpose. But I am not able to decide what can be the input parameters for our data.
The main problem is about custom word tagging. Our program should be able to tag unknown(not in training dataset) words to their most probable classes based on some score.
As I am new to NLP, I want to know what can be the next move forward.