Wikipedia-based Semantic Interpretation for Natural Language Processing

AAAI Conferences

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text.


Evaluating Older Users' Experiences with Commercial Dialogue Systems: Implications for Future Design and Development

arXiv.org Artificial Intelligence

Understanding the needs of a variety of distinct user groups is vital in designing effective, desirable dialogue systems that will be adopted by the largest possible segment of the population. Despite the increasing popularity of dialogue systems in both mobile and home formats, user studies remain relatively infrequent and often sample a segment of the user population that is not representative of the needs of the potential user population as a whole. This is especially the case for users who may be more reluctant adopters, such as older adults. In this paper we discuss the results of a recent user study performed over a large population of age 50 and over adults in the Midwestern United States that have experience using a variety of commercial dialogue systems. We show the common preferences, use cases, and feature gaps identified by older adult users in interacting with these systems. Based on these results, we propose a new, robust user modeling framework that addresses common issues facing older adult users, which can then be generalized to the wider user population.


The Numbers Behind the First FDA-Approved Autonomous AI Diagnostic System

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The first artificial intelligence (AI) diagnostic system to gain clearance from the U.S. Food and Drug Administration beat out all predetermined benchmarks, achieving "high diagnostic accuracy" for patients with certain forms of diabetic retinopathy, according to clinical trial findings. IDx, the developer of the system, IDx-DR, published its results this week in the peer-reviewed journal Nature Digital Medicine. The paper provides an inside look into a technology that could transform how the industry diagnoses diabetic retinopathy, a condition that can cause blindness, bringing the process from the specialist's office to primary care -- without the need for a clinician to interpret the results. READ: First-of-Its-Kind AI Tool for Diabetic Retinopathy Detection Approved by FDA "This is formerly uncharted territory in healthcare, making it especially critical that we ensure the highest level of safety before introducing autonomous AI into patient care," Michael D. Abràmoff, M.D., Ph.D., IDx's founder and president and the study's principal investigator, said in a statement. In April, the FDA cleared IDx-DR, which analyzes images of the eye, for detection of "more than mild" diabetic retinopathy in adults with diabetes.


Wikipedia-based Semantic Interpretation for Natural Language Processing

Journal of Artificial Intelligence Research

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.


Artificial Intelligence Effectively Assesses Cell Therapy Functionality

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A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.