Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: WISARD·a radical step forward in image recognition
Abstract: The WISARD recognition system invented at Brunei University has been developed into an industrialised product by Computer Recognition Systems under licence from the British Technology Group. Using statistical pattern classification it already shows great potential in rapid sorting, and research indicates that it will track objects with positional feedback, rather like the human eye.
Extracted statement — utilizes: WiSARD
Research field: Artificial Intelligence
Title: SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Abstract: Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
Extracted statement — Data annotation granularities: words
Research field: Artificial Intelligence
Title: Knowledgeable reader: Enhancing cloze-style read- ing comprehension with external commonsense knowledge
Abstract: We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer. This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer. Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models. By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Answering science exam questions using query reformulation with background knowledge
Abstract: Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that reformulates a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query reformulation, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Knowledge-based conversational agents and virtual story telling
Abstract: We describe an architecture for building speech-enabled conversational agents, deployed as self-contained Web services, with ability to provide inference processing on very large knowledge bases and its application to voice enabled chatbots in a virtual storytelling environment. The architecture integrates inference engines, natural language pattern matching components and story-specific information extraction from RDF/XML files. Our Web interface is dynamically generated by server side agents supporting multi-modal interface components (speech and animation). Prolog refactorings of the WordNet lexical knowledge base, FrameNet and the Open Mind common sense knowledge repository are combined with internet meta-search to provide high-quality knowledge sources to our conversational agents. An example of conversational agent with speech capabilities is deployed on the Web at http://logic.csci.unt.edu:8080/wordnet_agent/frame.html. The agent is also accessible for live multi-user text-based chat, through a Yahoo Instant Messenger protocol adaptor, from wired or wireless devices, as the jinni_agent Yahoo IM "handle".
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Towards a knowledge graph based speech interface
Abstract: Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognised text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. These semantic annotations of recognised words can be represented as a subject-predicate-object triples which collectively form a graph often referred to as a knowledge graph. This type of knowledge representation facilitates to use speech interfaces with any spoken input application, since the information is represented in logical, semantic form, retrieving and storing can be followed using any web standard query languages. In this work, we develop a methodology for linking speech input to knowledge graphs and study the impact of recognition errors in the overall process. We show that for a corpus with lower WER, the annotation and linking of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight, a tool to interlink text documents with the linked open data is used to link the speech recognition output to the DBpedia knowledge graph. Such a knowledge-based speech recognition interface is useful for applications such as question answering or spoken dialog systems.
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Algorithmic transparency of conversational agents
Abstract: A lack of algorithmic transparency is a major barrier to the adoption of artificial intelligence technologies within contexts which require high risk and high consequence decision making. In this paper we present a framework for providing transparency of algorithmic processes. We include important considerations not identified in research to date for the high risk and high consequence context of defence intelligence analysis. To demonstrate the core concepts of our framework we explore an example application (a conversational agent for knowledge exploration) which demonstrates shared human-machine reasoning in a critical decision making scenario. We include new findings from interviews with a small number of analysts and recommendations for future research.
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Knowledge-based transfer learning explanation
Abstract: Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper , we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DB-pedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.
Extracted statement — Machine Learning Input: text
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Material: text
Research field: Arts and Humanities
Title: Are the FAIR Data Principles fair?
Abstract: This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, and will be underpinned with feedback from the repositories. The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR - Findable, Accessible, Interoperable, Reusable. These principles have already gained traction within the research world. The European Commission has recently expanded its demand for research to produce open data. The relevant guidelines1are explicitly written in the context of the FAIR Data Principles. Given an increasing number of researchers will have exposure to the guidelines, understanding their viability and suggesting where there may be room for modification and adjustment is of vital importance. This practice paper is connected to a dataset(Dunning et al.,2017) containing the original overview of the sample group statistics and graphs, in an Excel spreadsheet. Over the course of two months, the web-interfaces, help-pages and metadata-records of over 40 data repositories have been examined, to score the individual data repository against the FAIR principles and facets. The traffic-light rating system enables colour-coding according to compliance and vagueness. The statistical analysis provides overall, categorised, on the principles focussing, and on the facet focussing results. The analysis includes the statistical and descriptive evaluation, followed by elaborations on Elements of the FAIR Data Principles, the subject specific or repository specific differences, and subsequently what repositories can do to improve their information architecture.
Extracted statement — Publication type: paper
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: Access
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: Clinicians
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: Collaboration
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: Devices
Research field: Bioinformatics
Title: Multiagent cooperation and competition with deep reinforcement learning
Abstract: Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.
Extracted statement — uses: Pong