3

Spacy's Sentencizer is very simple. However, Spacy 3.0 includes Sentencerecognizer which basically is a trainable sentence tagger and should behave better. Here is the issue with the details of its inception. You can train it if you have segmented sentence data. Another option is using NLTK's sent_tokenize, which should give better results than Spacy's ...


2

There is nothing in SpaCy that you can use out-of-the-box. However, they allow you to use custom components To solve your problem, I see at least three ways to do it. NTLK NLTK allows you to add known abbreviations as exceptions. See this StackOverflow post. Use a regular expression Since your problem is that you have some example of dots that shouldn't ...


1

I think the closest standard NLP task would be relationship extraction. In general it's a quite complex task which involves NER, syntactic analysis and semantic role labeling. Note that there are various works using the term "event extraction" (for example this), but as far as I know there is no clear definition of the task. It's often related to ...


1

Interesting task :) I think even with a good amount of training data it will be difficult for a regular NER model to perform well with new books titles and authors: The book may contain persons names which are not authors. The book titles are difficult to identify as such in general. For example "the Republic" might or might not be about the book, ...


Only top voted, non community-wiki answers of a minimum length are eligible