Medical WordNet: A new methodology for the construction and validation of information resources for consumer health

A consumer health information system must be able to comprehend both expert and non-expert medical vocabulary and to map between the two. We describe an ongoing project to create a new lexical database called Medical WordNet (MWN), consisting of medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications

Towards new information resources for public health: From WordNet to MedicalWordNet

In the last two decades, WORDNET has evolved as the most comprehensive computational lexicon of general English. In this article, we discuss its potential for supporting the creation of an entirely new kind of information resource for public health, viz. MEDICAL WORDNET. This resource is not to be conceived merely as a lexical extension of the original WORDNET to medical terminology; indeed, there is already a considerable degree of overlap between WORDNET and the vocabulary of medicine. Instead, we propose a new type of repository, consisting of three large collections of (1) medically relevant word forms, structured along the lines of the existing Princeton WORDNET; (2) medically validated propositions, referred to here as medical facts, which will constitute what we shall call MEDICAL FACTNET; and (3) propositions reflecting laypersons’ medical beliefs, which will constitute what we shall call the MEDICAL BELIEFNET. We introduce a methodology for setting up the MEDICAL WORDNET. We then turn to the discussion of research challenges that have to be met in order to build this new type of information resource

Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications

Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications

Rodeo and the Women\u27s Barrel Racing Team at Cal Poly

In the 1980 El Rodeo there is a picture captioned, “The smallest barrel racers in town.”1 It is a picture of an Australian shepherd dog used in the herding events working the barrels with a monkey strapped to its back. It emphasizes the fact that barrel racing is always entertaining, especially with “smaller” barrel racers. This paper will look at the history of rodeo at California Polytechnic State University San Luis Obispo and how it affected student life. It will also look at a few different schools that had the capacity for a rodeo team. Rodeo has been an important sport for many years at Cal Poly and this paper will look at its growing popularity in the 1950s-60s and how gender played a role in the competition events. Rodeo was the front-runner for women sports and a progressive step forward to gender equality in athletics. 1980 El Rodeo Yearbook, (California Polytechnic State University, San Luis Obispo), pg. 6

The Long-Short Story of Movie Description

Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII Movie Description allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the challenging setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these visual classifiers we learn how to generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD dataset. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis

Fighting with the Sparsity of Synonymy Dictionaries

Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of the sparsity of the synonymy dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science (LNCS

WordNet: An Electronic Lexical Reference System Based on Theories of Lexical Memory

Cet article fait la description de WordNet, système de référence électronique, dont le dessin est basé sur des théories psycholinguistiques concernant la mémoire lexicale et l’organisation mentale des mots.Les noms, les verbes et les adjectifs anglais sont organisés en groupes synonymes (les « synsets »), chacun représentant un concept lexical. Trois relations principales — l’hyponymie, la méronymie et l’antonymie — servent à établir les rapports conceptuels entre les « synsets ». Les présuppositions qui lient les verbes sont indiquées ainsi que leurs contextes syntaxiques et sémantiques.En tâchant de miroiter l’organisation mentale des concepts lexicaux, WordNet pourrait servir l’utilisateur sans formation en linguistique.This paper describes WordNet, an on-line lexical reference system whose design is based on psycholinguistic theories of human lexical organization and memory.English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Synonym sets are then related via three principal conceptual relations: hyponymy, meronymy, and antonymy. Verbs are additionally specified for presupposition relations that hold among them, and for their most common semantic/syntactic frames.By attempting to mirror the organization of the mental lexicon, WordNet strives to serve the linguistically unsophisticated user