7:00 – 4:00 ASRC PhD/NASA
- Looks like Aaron has added two users
- Create a “coherence” matrix, where the threshold is based on an average of one or more previous cells. The version shown below uses the tf-idf matrix as a source and checks to see if there are any non-zero values within an arbitrary span. If there are, then the target matrix (initialized with zeroes) is incremented by one on that span. This process iterates from a step of one (the default), to the specified step size. As a result, the more contiguous nonzero values are, the larger and more bell-curved the row sequences will be:
- Create a “details” sheet that has information about the database, query, parameters, etc. Done.
- Set up a redirect so that users have to go through the IRB page if they come from outside the antibubbles site
- It’s the End of News As We Know It (and Facebook Is Feeling Fine)
- And as the platforms pumped headlines into your feed, they didn’t care whether the “news” was real. They didn’t want that responsibility or expense. Instead, they honed in on engagement—did you click or share, increasing value to advertisers?
- Diversity (responsibility, expense), Stampede (engagement, share)
- And as the platforms pumped headlines into your feed, they didn’t care whether the “news” was real. They didn’t want that responsibility or expense. Instead, they honed in on engagement—did you click or share, increasing value to advertisers?
- Finished Analyzing Discourse and Text Complexity for Learning and Collaborating, and created this entry for the notes.
- Was looking at John Du Bois paper Towards a dialogic syntax, which looks really interesting, but seems like it might be more appropriate for spoken dialog. Instead, I think I’ll go to Claire Cardie‘s presentation on chat argument analysis at UMD tomorrow and see if that has better alignment.
- Argument Mining with Structured SVMs and RNNs
- We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.
- Argument Mining with Structured SVMs and RNNs
viztales 