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Posts Tagged ‘Linguist’

Robust Inference and Slacker Semantics

In preparing for some natural language generation[1], I came across some work on natural logic[2][3] and reasoning by textual entailment[4] (RTE) by Richard Bergmair in his PhD at Cambridge: Monte Carlo Semantics: Robust Inference and Logical Pattern Processing with Natural Language Text The work he describes overlaps our approach to robust inference from the deep, [...]

It’s hard to reckon nice English

The title is in tribute to Raj Reddy’s classic talk about how it’s hard to wreck a nice beach. I came across interesting work on higher order and semantic dependency parsing today: Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers. Priberam: A turbo semantic parser with second order features So I gave the software [...]

Properly disambiguating a sentence using the Linguist™

Consider the following disambiguation result from a user of Automata’s Linguist™.

Deep Parsing vs. Deep Learning

For those of us that enjoy the intersection of machine learning and natural language, including “deep learning”, which is all the rage, here is an interesting paper on generalizing vector space models of words to broader semantics of English by Jayant Krishnamurthy, a PhD student of Tom Mitchell at Carnegie Mellon University: Krishnamurthy, Jayant, and [...]

Affiliate Transactions covered by The Federal Reserve Act (Regulation W)

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 [...]

Automatic Knowledge Graphs for Assessment Items and Learning Objects

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 [...]

Artificially Intelligent Educational Technology

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 [...]

Knowledge acquisition using lexical and semantic ontology

In developing a compliance application based on the institutional review board policies of John Hopkins’ Dept. of Medicine, we have to clarify the following sentence: Projects involving drugs or medical devices other than the use of an approved drug or medical device in the course of medical practice and projects whose data will be submitted [...]

Neat vs. Scruffy and Watson

Recently, John Sowa has commented on LinkedIn or in correspondence with some of us at Coherent Knowledge Systems on the old adage due to Shanks concerning the Neats. vs. the Scruffies.  The Neats want nice formal logics as the basis of artificial intelligence.  This includes anyone who prefers classical logic (e.g., Common Logic, RIF-BLD, or [...]

Pedagogical applications of proofs of answers to questions

In Vulcan’s Project Halo, we developed means of extracting the structure of logical proofs that answer advanced placement (AP) questions in biology.  For example, the following shows a proof that separation of chromatids occurs during prophase. This explanation was generated using capabilities of SILK built on those described in A SILK Graphical UI for Defeasible [...]