How to extract particular word(s) from the list of sentences using Python NLP. These word(s) are Parts of Medical equipments
It sounds like you would benefit from looking at Named Entity Recognition (NER). I would be curious if SpaCy would be able to pick these out as PRODUCT entities.
I want to extract Medical Equipment part names from the list of sentences.
These sentences are recorded actions taken on a support request which might include replacement of a part or acknowledging a part is in a bad state.
Here are the sample sentences-

Found [Some equipment part] on both side not working.
Narrowed problem down to the [Some equipment part].
Needed a [Some equipment part] replacement.
Assisted with troubleshooting, found [Some equipment part] is most likely bad.
[Some equipment part] won't go down, will order part.

I want to extract "[Some equipment part]" from the above sentences.
Things I've already tried-
First, I've filtered the sentences using Sentiment Analysis. Considering only the ones which have a negative sentiment or have "replace" text in them.

Using NLTK, After POS tagging using RegexpParser on a defined grammer = "NP : {<VB.><NN.>+<NN.>+|<VB.><NN.>+}"
Using Spacy, After POS tagging and dependency, filtering based on Verb, Noun relationship - token.dep_ in ['dobj'] and token.pos_ == 'NOUN'

The above approaches gives me a lot of meaningless output.
Please, let me know if there is anything which can be of help.