Implicit Knowledge, Question Answering, and the Representation of Expository Text

2017 ◽  
pp. 109-171
Author(s):  
Arthur C. Graesser ◽  
Sharon M. Goodman
Keyword(s):  
Question Answering ◽  
Expository Text ◽  
Implicit Knowledge ◽  
Knowledge Question

scholarly journals Bio-AnswerFinder: a system to find answers to questions from biomedical texts

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Ibrahim Burak Ozyurt ◽  
Anita Bandrowski ◽  
Jeffrey S Grethe
Keyword(s):  
Question Answering ◽  
Keyword Search ◽  
Short Term Memory ◽  
Document Retrieval ◽  
Implicit Knowledge ◽  
Biomedical Literature ◽  
Easy Access ◽  
Blind Test ◽  
Rate Of Increase ◽  
Biomedical Texts

Abstract The ever accelerating pace of biomedical research results in corresponding acceleration in the volume of biomedical literature created. Since new research builds upon existing knowledge, the rate of increase in the available knowledge encoded in biomedical literature makes the easy access to that implicit knowledge more vital over time. Toward the goal of making implicit knowledge in the biomedical literature easily accessible to biomedical researchers, we introduce a question answering system called Bio-AnswerFinder. Bio-AnswerFinder uses a weighted-relaxed word mover's distance based similarity on word/phrase embeddings learned from PubMed abstracts to rank answers after question focus entity type filtering. Our approach retrieves relevant documents iteratively via enhanced keyword queries from a traditional search engine. To improve document retrieval performance, we introduced a supervised long short term memory neural network to select keywords from the question to facilitate iterative keyword search. Our unsupervised baseline system achieves a mean reciprocal rank score of 0.46 and Precision@1 of 0.32 on 936 questions from BioASQ. The answer sentences are further ranked by a fine-tuned bidirectional encoder representation from transformers (BERT) classifier trained using 100 answer candidate sentences per question for 492 BioASQ questions. To test ranking performance, we report a blind test on 100 questions that three independent annotators scored. These experts preferred BERT based reranking with 7% improvement on MRR and 13% improvement on Precision@1 scores on average.

Intelligent pig raising knowledge question‐answering system based on neural network schemes

2021 ◽  
Author(s):  
Hsu‐Yang Kung ◽  
Ren‐Wu Yu ◽  
Chi‐Hua Chen ◽  
Chan‐Wei Tsai ◽  
Chia‐Yu Lin
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Knowledge Question

scholarly journals Modelling and Implementation of a Knowledge Question-answering System for Product Quality Problem Based on Knowledge Graph

2021 ◽  
Vol 2068 (1) ◽  
pp. 012051
Author(s):  
Hanxu Liu ◽  
Fangxu Dong ◽  
Meiqing Wang ◽  
Qiu Lin
Keyword(s):  
Product Quality ◽  
Template Matching ◽  
Process Model ◽  
Question Answering ◽  
Knowledge Graph ◽  
Knowledge Retrieval ◽  
Semantic Classification ◽  
Question Answering System ◽  
Quality Problem ◽  
Knowledge Question

Abstract Aiming at the problem of difficulty in understanding the semantics of the problem in the traditional quality problem management system, the knowledge retrieval technology of product quality problem based on the knowledge graph is carried out. The process model for knowledge retrieval of quality problem based on semantic templates is constructed. A domain corpus is built, which consisting of thousands of quality problem handling records. The TF-IDF (Term Frequency-inverse Document Frequency) algorithm was used to extracted the vocabulary from the quality problem analysis reports. A natural language question semantic classification process model based on Naive Bayes classifier is established to improve the accuracy of semantic template matching. On the basis of theoretical study, a quality problem knowledge question-answering system-QQ-KQAS based on knowledge graph is developed, and the effectiveness of the proposed method is verified through examples.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Text Comprehension: Graphic Organizers to the Rescue

2009 ◽  
Vol 10 (3) ◽  
pp. 82-89
Author(s):  
Janet L. Proly ◽  
Jessica Rivers ◽  
Jamie Schwartz
Keyword(s):  
Reading Comprehension ◽  
Text Comprehension ◽  
Graphic Organizers ◽  
Expository Text ◽  
Supporting Evidence

Abstract Graphic organizers are a research based strategy used for facilitating the reading comprehension of expository text. This strategy will be defined and the evolution and supporting evidence for the use of graphic organizers will be discussed. Various types of graphic organizers and resources for SLPs and other educators will also be discussed.

Patterns of Implicit Learning Below the Level of Conscious Knowledge

2010 ◽  
Vol 24 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Juliana Yordanova ◽  
Rolf Verleger ◽  
Ullrich Wagner ◽  
Vasil Kolev
Keyword(s):  
Implicit Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Knowledge Generation ◽  
Sleep Stages ◽  
Implicit Processing ◽  
Implicit Processes ◽  
Late Night ◽  
On Line ◽  
On Line Learning

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

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