I am doing a research regarding on automatic text summarizing. So in order to weighting sentences I need to get words related to a particular field or domain like shown below.
Topic word - Car
Related words - engine, driver, road, break, accelerator
Is there any direct method that I can use like wordnet synsets.
One very obvious way is to use gensim's word2vec most_similar() function to get the most related words to the queried word.
You can check an online version of how it works out here: https://projector.tensorflow.org/
Again it depends on what kind of relations you seek. Other useful methods include:
from nltk.corpus import wordnet as wn
wn.synsets('car')
wn.synsets('car')[0].hyponyms()
