Benjamin Grosof, co-founder of Coherent Knowledge Systems, is also involved with developing a standard ontology for the financial services industry (i.e., FIBO). In the course of working on FIBO, he is developing a demonstration of defeasible logic concerning Regulation W of the The Federal Reserve Act. Regulation W specifies which transactions involving banks and their affiliates are prohibited under Section 23A of the Act. In the course of doing this, there are various documents which are being captured within the Linguist™ platform. This is a brief note of how those documents can be imported into the platform for curation into formal semantics and logic (as Benjamin and Coherent are doing). Continue reading “Affiliate Transactions covered by The Federal Reserve Act (Regulation W)”
As I mentioned in this post, we’re having fun layering questions and answers with explanations on top of electronic textbook content.
The basic idea is to couple a graph structure of questions, answers, and explanations into the text using semantics. The trick is to do that well and automatically enough that we can deliver effective adaptive learning support. This is analogous to the knowledge graph that users of Knewton‘s API create for their content. The difference is that we get the graph from the content, including the “assessment items” (that’s what educators call questions, among other things). Essentially, we parse the content, including the assessment items (i.e., the questions and each of their answers and explanations). The result of this parsing is, as we’ve described elsewhere, precise lexical, syntactic, semantic, and logic understanding of each sentence in the content. But we don’t have to go nearly that far to exceed the state of the art here. Continue reading “Automatic Knowledge Graphs for Assessment Items and Learning Objects”
We’re collaborating on some educational work and came across this sentence in a textbook on finance and accounting:
- All of these are potentially good economic decisions.
We use statistical NLP but assist with the ambiguities. In doing this, we relate questions and answers and explanations to the text.
We also extract the terminology and produce a rich lexicalized ontology of the subject matter for pedagogical uses, assessment, and adaptive learning.
Here’s one that just struck me as interesting. This is a case where the choice looks like it won’t matter much either way, but …
Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams. Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially intelligent systems that answer questions and support decisions.