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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.

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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()
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