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Hi, I'm currently compiling additional, more modern html documents with gold standard content + comments for use in training dragnet models, and I have a few questions:
Should I consider the text of embedded tweets, posts, quotations, image captions, and other rich media to be content?
Should I include author byline, pubdate, etc. at the start of an article as content? What about typical addenda at the bottom of the article?
All those will depend on what your final goals are for the model. The type of training data you collect and use will determine the final biases of your model. I'd say if you want those things to be considered content then add them to the gold standard, otherwise exclude. In our case, we made a decision to use three classes of output for each block (chrome, content and comments) based on product requirements at the time. If I were to collect data again I'd be tempted to add more classes like some of the things you mentioned (byline, pubdate, etc.) to capture more information.
Sure, makes sense. I'm really only interested in "content", but its definition is a bit subjective, and could certainly be broken up into subclasses. I don't thinkdragnet's data processing and training code is set up to handle any additional classes such as "content metadata" (byline, pubdate, and such), right?
Not directly but it wouldn't be too hard to add. You could probably just replace the block_model with a multiclass classifier and it would mostly work.
Okay, one more (last?) question: How important is it to maintain whitespace when manually copy/pasting the content data from the raw html rendered in a browser, or is that all handled in dragnet's data_processing.py? Things get tricky when there are images and such between blocks of content...
It isn't important - dragnet tokenizes the gold standard and web page text then aligns the blocks to gold standard based on overlapping tokens. So as long as you leave some space between each content block it'll handle the test.
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matt-peters commentedon Jan 9, 2017
All those will depend on what your final goals are for the model. The type of training data you collect and use will determine the final biases of your model. I'd say if you want those things to be considered content then add them to the gold standard, otherwise exclude. In our case, we made a decision to use three classes of output for each block (chrome, content and comments) based on product requirements at the time. If I were to collect data again I'd be tempted to add more classes like some of the things you mentioned (byline, pubdate, etc.) to capture more information.
bdewilde commentedon Jan 9, 2017
Sure, makes sense. I'm really only interested in "content", but its definition is a bit subjective, and could certainly be broken up into subclasses. I don't think
dragnet's data processing and training code is set up to handle any additional classes such as "content metadata" (byline, pubdate, and such), right?matt-peters commentedon Jan 9, 2017
Not directly but it wouldn't be too hard to add. You could probably just replace the
block_modelwith a multiclass classifier and it would mostly work.bdewilde commentedon Jan 9, 2017
Okay, one more (last?) question: How important is it to maintain whitespace when manually copy/pasting the content data from the raw html rendered in a browser, or is that all handled in dragnet's
data_processing.py? Things get tricky when there are images and such between blocks of content...matt-peters commentedon Jan 9, 2017
It isn't important - dragnet tokenizes the gold standard and web page text then aligns the blocks to gold standard based on overlapping tokens. So as long as you leave some space between each content block it'll handle the test.