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Posts Tagged ‘NLP’

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 [...]

Higher Education on a Flatter Earth

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 [...]

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 [...]

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 [...]

Acquring Rich Logical Knowledge from Text (Semantic Technology 2013)

As noted in prior posts about Project Sherlock, we have acquired knowledge from a biology textbook to build the business case for applications like Inquire.  We reported our results at SemTech recently.  The slides are  available here.

Background for our Semantic Technology 2013 presentation (part 1)

In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment.  This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education.  Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration [...]

Understanding English promotes better policies and requirements

Capturing some policies from a publication by the Health and Human Services department recently turned up the following…. It’s probably the case that there are more specific lists than just “some list” or “any list”, as suggested below. This is a good thing about applying deep natural language understanding to policy statements.  It helps you [...]

Logic from the English of Science, Government, and Business

Our software is translating even long and complicated sentences from regulations to textbooks into formal logic (i.e,, not necessarily first-order logic, but more general predicate calculus).   As you can see below, we can translate this understanding into various logical formalisms including defeasible first-order logic, which we are applying in Vulcan’s Project Halo.  This includes classical [...]

Project Sherlock

Working as part of Vulcan’s Project Halo[1], Automata is applying a natural language understanding system that translates carefully formulated sentences into formal logic so as to answer questions that typically require deeper knowledge and inference than demonstrated by Watson. The objective over the next three quarters is to acquire enough knowledge from the 9th edition [...]

NLP: depictive in an HPSG lexicon?

We’re having a great time using OWL to clarify and enrich the semantics of the rich model underlying the ERG. Here’s an example, FYI. If you’d like to know more (or help), please drop us a line! Overall the project will demonstrate our capabilities for transforming everyday sentences into RIF and business rule languages using SBVR extended with defeasibility and other capabilities, all modeled in the same OWL ontology.