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Category: Statistical-level Data Processing

How Ontologies Fit Into AI

How Ontologies Fit Into AI

Roy, A. Park, Y.J. and Pan, S.(2017, Sept. 21). Domain-SpecificWord Embeddings from Sparse Cybersecurity Texts, arXiv 1709.07470v1 [cs.CL]. pdf, accessed Apr. 25, 2018 by A.J.M. Abstract: Classic word embedding methods such as Word2Vec and GloVe work well when they are given a large text corpus. When the input texts are sparse as in many specialized domains (e.g., cybersecurity), these methods often fail to produce high-quality vectors. In this paper, we describe a novel method to train domain-specific word embeddings from…

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Ontologies, Knowledge Graphs, and AI: Getting from “Here” to “There” (Part 2)

Ontologies, Knowledge Graphs, and AI: Getting from “Here” to “There” (Part 2)

A Principled Approach to AI: Representations and Transitions:   In the last post, on “Moving Between Representation Levels: The Key to an AI System (Part 1),” I re-introduced one of the most important and fundamental AI topics: how we can effectively use multiple representation levels. If we’re going to build (or gauge the properties of) an AI system, we need a framework. The notion of representations, and of moving between representation levels, is as fundamental as we can get. In…

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Moving Between Representation Levels – the Key to Making an AI System Work (Part 1)

Moving Between Representation Levels – the Key to Making an AI System Work (Part 1)

Representation Levels: The Key to Understanding AI   “No computation without representation” Jerry Fodor (1975). The Language of Thought, p.34. online access.   One of the key notions underlying artificial intelligence (AI) systems is not only that of knowledge representation, but that a good AI system will successively move disparate pieces of low-level, or signal-level information up the abstraction ladder. For example, an image understanding system will have a low-level component that extracts edges and regions from the image (or…

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Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text by Matching Terms and Entities to Ontologies and Concepts Text analytics is the means by which computer algorithms can extract meaning and useful insights from raw text sources. This can have enormous impact in realms such as marketing, business intelligence, and political campaigns. However, text analytics is one of the toughest challenges in predictive analytics. The reason why this is so hard? It’s because – when done right – text analytics must effectively connect…

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Novelty Detection in Text Corpora

Novelty Detection in Text Corpora

Detecting Novelty Using Text Analytics Detecting novel events – new words, meaning new events – is one of the most important text analytics tasks, and is an important step towards predictive analytics using text mining. On July 24, 2015, The New York Times (and many other news sources) published an article identifying potential inclusion of classified information in the emails which Hillary Clinton had sent via private email and stored on her private email server. How would we use text…

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Survey of Text Mining II: Cluster-Preserving Dimension Reduction Methods (Chapter 1)

Survey of Text Mining II: Cluster-Preserving Dimension Reduction Methods (Chapter 1)

Some time ago, I promised a colleague a review of an excellent book’ Survey of Text Mining II: Clustering, Classification, and Retrieval, edited by Michael W. Berry and Malu Castellanos. Overall, this book would serve well as the basis for a one-semester graduate course in specialized methods for (textual) data analytics. It presupposes an expert’s (or at least a solid journeyman’s) understanding of basic algorithms along with the issues of textual data mining / analytics. Each chapter presents a new…

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