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Posts Tagged ‘NLP’

Combinatorial ambiguity? No problem!

Working on translating some legal documentations (sales and use tax laws and regulations) into compliance logic, we came across the following sentence (and many more that are even worse): Any transfer of title or possession, exchange, or barter, conditional or otherwise, in any manner or by any means whatsoever, of tangible personal property for a [...]

Simple, Fast, Effective, Active Learning

Recently, we “read” ten thousand recipes or so from a cooking web site.  The purpose of doing so was to produce a formal representation of those recipes for use in temporal reasoning by a robot. Our task was to produce ontology by reading the recipes subject to conflicting goals.  On the one hand, the ontology [...]

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 [...]

Smart Machines And What They Can Still Learn From People – Gary Marcus

This is a must-watch video from the Allen Institute for AI for anyone seriously interested in artificial intelligence.  It’s 70 minutes long, but worth it.  Some of the highlights from my perspective are: 27:27 where the key reason that deep learning approaches fail at understanding language are discussed 31:30 where the inability of inductive approaches [...]

Deep Learning, Big Data and Common Sense

Thanks to John Sowa’s comment on LinkedIn for this link which, although slightly dated, contains the following: In August, I had the chance to speak with Peter Norvig, Director of Google Research, and asked him if he thought that techniques like deep learning could ever solve complicated tasks that are more characteristic of human intelligence, [...]

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.