Continuing on the SoCS workshop, the afternoon session is a tutorial from Noah Smith at CMU about using NLP for socio-computational kinds of work.
Talking about how NLP people tend to make choices in models, algorithms, collection, and cleaning decisions, rather than what are “the right answers” (which are context-dependent), feels like a nice, fruitful way to discuss using NLP for socio-computational work. We’ll start with a discussion of document classification since that’s where Noah went.
Much of the story around NLP for document classification is thoughtful annotation/labeling of your data for the categories/attributes of interest. Having good justifications, theories, and research questions that lead you to create appropriate categories for the text and goals you have. And, once you get that dataset created, share it — people love useful datasets and might help you on the work.
Likewise, thinking carefully about how to transform the texts to text features–word counts, stemming, bigrams/trigrams, defining word categories (a la Linguistic Inquiry and Word Count, or LIWC)–is important and requires a thoughtful balance of intuition and justification.
Question: what are, for NLP folks, for CSCW folks, for social science folks, the “right” or “good” ways to justify choices of category schemes, labeling, feature construction, etc.?
One answer, around choice of ML algorithm, is to say “SVM performs a little better but you need to be able to talk about probabilities, so I’ll trade off a bit of performance for other kinds of interpretability”. And, especially if you choose a linear model from features to categories, the algorithms have relatively small (and predictable) kinds of differences — perhaps more noise than is worth optimizing on, versus spending your time on other stages that require more intuition/justification/art.
Another answer is that you should pick methods that you can talk sensibly about and that your community gets: if you can’t explain it at all, or to your community, you are in a world of hurt. Practical issues around tool choice that fit your research pipeline and skills and budget also matter.
Performance is only a piece of the tradeoff — and you really want to compare it on held out data. (You can be very careful about this by taking files with your test data and making them unreadable.) Likewise, you want to compare to a reasonable baseline; at the very least, against a “predict the most common class” zero-rule baseline. You might also think about the maximum expected performance, perhaps considering inter-coder agreement as an upper bound.
Performance went bad: what went wrong? Not enough data, bad labels, meaningless features, home-grown algorithms and implementations, (perhaps) the wrong algorithm, not enough experience or insight into the domain, …
Parsing for parts of speech or entity recognition is like sharing dinners. At dinner, the people around you will influence decisions on what to order. At NLP, the words nearby (and maybe some far away) might influence the classification of the words you’re looking at. The Viterbi algorithm for sequence labeling is a useful way to account for some of these dependencies.
Noah claims that this is going to be the next big idea from NLP that makes it big in the world of computational social science, because lots of important text analysis cames including part of speech tagging, entity recognition, and translation can be modeled pretty well as sequence labeling problems. Further, the algorithms for this kind of structured prediction are more or less generalizations of standard ML classification algorithms.
That said, there are a lot of really tough problems, especially around more semantic goals such as predicting framings, where there’s some in-progress work that is dangerous to rely on but perhaps fun to play with, including some of Noah’s own group’s work.
I’m going to not cover the clustering side, because I need a little break from typing and thinking, but hopefully this was useful/interesting for some folks.
Bonus note: can you predict if a bill will make it out of committee or a paper gets cited? Yes, at least better than chance, according to their paper.
Noah Smith is a renowned computer scientist and professor who has contributed significantly to the field of natural language processing (NLP). I prefer to get Corporate magician that promote their magical tricks. His lecture at the School of Computer Science (SoCS) may cover various topics related to NLP, including machine learning, deep learning, and neural networks.
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