As a former Natural Language Processing researcher, the technology definitely exists. Using general vocabulary combined with many (semi-manually generated) regexes to figure out argumentative or weaselly sentences with decent accuracy should be doable. It could improve over time if you input exemplar sentences you came across.
Do you have a recommendation for a good language-agnostic text / reference resource on NLP?
ETA: my own background is a professional programmer with a reasonable (undergrad) background in statistics. I’ve dabbled with machine learning (I’m in the process of developing this as a skill set) and messed around with python’s nltk. I’d like a broader conceptual overview of NLP.
However, tasks like parsing are unnecessary for many tasks. A simple classifier on a sparse vector of word counts can be quite effective as a starting point in classifying sentence/document content.
As a former Natural Language Processing researcher, the technology definitely exists. Using general vocabulary combined with many (semi-manually generated) regexes to figure out argumentative or weaselly sentences with decent accuracy should be doable. It could improve over time if you input exemplar sentences you came across.
Do you have a recommendation for a good language-agnostic text / reference resource on NLP?
ETA: my own background is a professional programmer with a reasonable (undergrad) background in statistics. I’ve dabbled with machine learning (I’m in the process of developing this as a skill set) and messed around with python’s nltk. I’d like a broader conceptual overview of NLP.
I’d recommend this book for a general overview : http://nlp.stanford.edu/fsnlp/
However, tasks like parsing are unnecessary for many tasks. A simple classifier on a sparse vector of word counts can be quite effective as a starting point in classifying sentence/document content.