What is the benefit of NLP sentence segmentation over Python algorithm?
The sentence segmentation routines from the NLP libraries like SpaCy, NLTK, etc. handle edge cases much better and are more robust to handling punctuation and context. For example, if you choose to split sentences by treating a '.' as a sentence boundary, how would you handle a sentence like - "There are 0.5 liters of water in this bottle."?
I have a task in NLP to do a sentence segmentation, but I wonder, what are the advantages of using built-in NLP sentence segmentation algorithms, such as Spacy, NLTK, BERT etc, over Python '.' separator or similar algorithm?
Is it the speed? or accuracy? or less line of code?
How different or strong these algorithms over the ones that we can build ourselves in Python?