Text Graphs & Natural Language 2017


Notes:

A text graph is a graph representation of a text item, such as document, passage or sentence.

  • NLG Pipeline Architecture

Wikipedia:

References:

See also:

Dynamic Topic ModelingSentence Summarization


STRICT: Information retrieval based search term identification for concept location
MM Rahman, CK Roy – Software Analysis, Evolution and …, 2017 – ieeexplore.ieee.org
… GRAPH BASED TERM-WEIGHTING In information retrieval (IR), natural language text is often transformed into a … 1-(b) shows the text graph of the example change request (ie, Listing 1 … Once text graphs are developed, we apply two adapted versions of the popular algorithm by …

Measuring Perceived Causal Relationships Between Narrative Events with a Crowdsourcing Application on Mturk
D Hu, DA Broniatowski – … Behavioral-Cultural Modeling and Prediction and …, 2017 – Springer
… Thus, we built an event parser based on natural language processing using NLTK [1] to consolidate narrative events … a graph, given that we have 22 narrative events, and excluding self-links: $$ {\text{P}}\left( {\text{link}} \right) < \frac{{{\text{P }}\left( {\text{graph}} \right)}}{{mathbf …

On Joint Representation Learning of Network Structure and Document Content
J Schlötterer, C Seifert, M Granitzer – International Cross-Domain …, 2017 – Springer
… like Deepwalk [9], LINE [11] or Node2Vec [3], that build on the representation learning techniques from the natural language processing domain … 6. The illustrated model corresponds to the PV-DM approach, hence we refer to this architecture as text graph distributed memory (TG …

A context-based regularization method for short-text sentiment analysis
Z Xiangyu, L Hong, W Lihong – Service Systems and Service …, 2017 – ieeexplore.ieee.org
… author Abstract—Sentiment analysis is an important task in natural language processing, which has promises great value to areas of interests such as business, politics and other fields … The first step is to build the text-graph. We …

Exploring complex and big data
J Stefanowski, K Krawiec, R Wrembel – International Journal of …, 2017 – degruyter.com
… models, have proven deep learning to be capable of effective learning from data representations that are arguably ‘even less tabular’: variable-length sequences (time series, sound, speech, text), graphs and networks (including social networks), natural language, and even the …

Matching, Reranking and Scoring: Learning Textual Similarity by Incorporating Dependency Graph Alignment and Coverage Features
S Kohail, C Biemann – 18th International Conference on …, 2017 – inf.uni-hamburg.de
… Measuring textual similarity, resulting from paraphrasing or summa- rization, may improve language understanding for many Natural Language Processing (NLP) applications … certain word in one dependency graph cannot be mapped to any word in the query text graph, as well …

A Keyword Extraction Approach for Single Document Extractive Summarization Based on Topic Centrality
VVMK Ravinuthala, SR Chinnam – inass.org
… Topic Association Graph has a better definition for edge strength when compared to that of Thematic text graph … keyword extraction given more linguistic knowledge”, In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Association for …

SIE: Characterize iSchool research territory via scholarly data
X Liu, W Lu, Y Ding, S Wunells – iConference 2017 Proceedings …, 2017 – ideals.illinois.edu
… Computing, Indiana University Bloomington. His research interests include information retrieval, natural language processing, text/graph mining, digital library, metadata, and human computing. His dissertation at Syracuse University …

Visualising Close Call in railways: a step towards Big Data Risk Analysis Original Citation FigueresEsteban, Miguel, Hughes, Peter and Van Gulijk, Coen (2015) …
M FigueresEsteban – pdfs.semanticscholar.org
… For these approaches diverse text mining techniques (eg for automated information retrieval), natural language processing techniques (eg for tokenization, stemming or parsing … Key attributes in a text-graph analysis are: the degree of a node as an indicator of the importance of a …

Large-Scale Occupational Skills Normalization for Online Recruitment.
F Javed, P Hoang, T Mahoney, M McNair – AAAI, 2017 – aaai.org
Page 1. Large-Scale Occupational Skills Normalization for Online Recruitment Faizan Javed, Phuong Hoang, Thomas Mahoney, Matt McNair CareerBuilder 5550-A Peachtree Parkway Peachtree Corners, GA 30092 {Faizan …

Catalyst: Piloting Capabilities for more Transparent Text Analytics
F Zablith, B Azad, I Osman – 2017 – aisel.aisnet.org
… We rely on the Stanford Natural Language Processing (NLP) tools (De Marneffe and Manning 2008) for the text preprocessing tasks … After transforming the text input into a linked graph, the objective of the Sentiment Analysis module is to enrich the text graph with polarity …

Text Analysis Using Different Graph-Based Representations
E Castillo, O Cervantes, D Vilarino – Computación y Sistemas, 2017 – redalyc.org
Page 1. Computación y Sistemas ISSN: 1405-5546 computacion-y-sistemas@cic.ipn.mx Instituto Politécnico Nacional México Castillo, Esteban; Cervantes, Ofelia; Vilariño, Darnes Text Analysis Using Different Graph-Based Representations Computación y Sistemas, vol …

TM-SGTD: Text Mining Based Semantic Graph for Text Document Approach for Text Representation
A Pacharne, PS Nair, DS Rao – enggjournals.com
… also supports the grammatical associations among the words or semantic similarities in any homogeneous text graph representation … [6] Liu, Jianyi, and Jinghua Wang, “Keyword extraction using language network”, International Conference on Natural Language Processing and …

A Dwarf-based Scalable Big Data Benchmarking Methodology
W Gao, L Wang, J Zhan, C Luo, D Zheng, Z Jia… – arXiv preprint arXiv …, 2017 – arxiv.org
… out a broad spectrum of big data ana- lytics workloads (machine learning, data mining, com- puter vision and natural language processing) through … vide various data inputs with different data types and distributions to the dwarf components, covering text, graph and matrix data …

SSDM2: a Two-Stage Semantic Sequential Dependence Model Framework for Biomedical Question Answering
BW Zhang, XC Yin – Cognitive Computation, 2017 – Springer
… with the “rough answers”—relevant documents and the “exact answers”—natural language sen- tences … are several cognitive-inspired attempts in lan- guage understanding or text/graph matching in … The abovementioned only considered semantic informa- tion for text/graphs …

Exploring NLP web APIs for building Arabic systems
SA Al-Ghamdi, J Khabti… – … (ICDIM), 2017 Twelfth …, 2017 – ieeexplore.ieee.org
… I. INTRODUCTION Natural Language Processing (NLP) is defined as a computational process that analyzes natural language units in different levels … 2 Babelfly http://babelfy.org/guide Disambiguating text Text/Graph represent the meaning of concept or named entities Lexical …

Graph Mining to Characterize Competition for Employment
A Toulis, L Golab – Proceedings of the 2nd International Workshop on …, 2017 – dl.acm.org
… A graph-based approach to skill extraction from text. Graph-Based Methods for Natural Language Processing, page 79, 2013. [10] R. Lambiotte, J.-C. Delvenne, and M. Barahona. Laplacian dynamics and multiscale modular structure in networks …

Improving Triangle-Graph Based Text Summarization using Hybrid Similarity Function
YA AL-Khassawneh, N Salim, M Jarrah – Indian Journal of Science and …, 2017 – indjst.org
… When using the graph models for the natural language text documents, build a graph which symbolises the document text and connects the words and the text having meaningful rela- tionships31,32 … Summary END Text-Graph representation Features Selection and Scoring …

A Question and Answering System for Management of Cloud Service Level Agreements
S Mittal, A Gupta, KP Joshi, C Pearce… – … (CLOUD), 2017 IEEE …, 2017 – ieeexplore.ieee.org
… WOLFIE [16] was a closed domain question answering system which used inductive logic programming methods to map natural language queries into executable logical form. III … The information extracted would then be displayed to the user in form of text, graphs or tables …

Virtual Reading: The Prospero Project Redux
WL Benzon – 2017 – papers.ssrn.com
… the 1950s. At that time a number of reasearch groups were working on cognitive or semantic network models for natural language semantics. It was bleeding edge research at the time. I … graph. Call it the text graph. Connect each …

Business Popularity Analysis from Twitter
P Yaisawas, S Lerdsri, B Thanasopon… – … on Computing and …, 2017 – Springer
… 4.4 The Web Application. The analysis results as described on the previous section are displayed as numbers, text, graphs and charts on our business popularity web application … In: The 2002 Conference on Empirical Methods in Natural Language Processing, pp. 79–86 …

Learning Technology Research Center, National Central University, Chungli, Taiwan b Graduate Institute of Network Learning Technology, National Central …
CY Chou, TW Chan, CJ Lin – researchgate.net
… information. The interface may be composed of buttons, menus, text, graph, voice, animation, multimedia, virtual reality, or other advanced techniques. An interface usually processes natural language to facilitate communication between ITSs and the student …

A Novel Approach to Analyzing Collaborative Knowledge Building in Collaborative Learning
L Zheng – Knowledge Building and Regulation in Computer …, 2017 – Springer
… objectives, knowledge, facts and examples, management instructions, relevant information, and off-topic information; the representation format denotes text, graph, table, sound … The nature of this method is to map information flows onto the knowledge map by natural language …

Automated assessment of the quality of peer reviews using natural language processing techniques
L Ramachandran, EF Gehringer, RK Yadav – International Journal of …, 2017 – Springer
… We use natural language processing and machine-learning techniques to calculate these metrics … Haghighi et al. (2005) use dependency trees to determine text entailment. They use node and path substitutions to compare text graphs …

Learning Knowledge Graph Embeddings for Natural Language Processing
M Chen – 2017 – pdfs.semanticscholar.org
Page 1. Learning Knowledge Graph Embeddings for Natural Language Processing Muhao Chen Department of Computer Science University of California, Los Angeles Winter 2017 Page 2. Abstract … 12 2.3 Natural Language Processing Tasks …

Paper2vec: combining graph and text information for scientific paper representation
S Ganguly, V Pudi – European Conference on Information Retrieval, 2017 – Springer
… of the so called word, document embeddings and achieve state-of-the-art performances throughout the breadth of Natural Language Processing (NLP … baseline: We concatenated Paragraph Vector with Deepwalk embeddings to serve as a baseline for our text-graph combination …

An Efficient Approach for Keyphrase Extraction from English Document
IH Emu, AU Ahmed, MM Islam, MS Al Mamun… – 2017 – mecs-press.org
… “Improved automatic keyword extraction given more linguistic knowledge”, In Proceedings of the 2003 Conference on Emprical Methods in Natural Language Processing, pages 216–223 … [17] Murali Krishna VV Ravinuthala, Satyananda Reddy Ch., Thematic “Text Graph: A Text …

Unit-7 Courseware Writing
S Kumar, A Saxena, M Jauhari, K Barik – 2017 – 14.139.40.199
… Title: , Time: Date: Objective: Text:_ Graphs Menu Options Orientation Date Text Graph Menus Options Next Fig 73 Page 7 … He/She can enter his/her response in natural language (a language used by human beings in day-to-day life as Hindi, English, etc.) …

JIRKM| Journal of Information Retrieval and Knowledge Management
SAM Noah, FD Ahmad, RA Kadir, A Azman, M Mohd… – 2017 – umexpert.um.edu.my
… Many researches have applied graph-based text representation for natural language text. Graphs have been applied for text summarization (Erkan & Radev, 2004; Mihalcea & Tarau, 2004 ), keyword/keyphrase extraction (Mihalcea & Tarau, 2004; Palshikar, 2007; Liu et al …

Poincaré embeddings for learning hierarchical representations
M Nickel, D Kiela – Advances in Neural Information Processing …, 2017 – papers.nips.cc
… Learning representations of symbolic data such as text, graphs and multi-relational data has become a central paradigm in machine learning … Prominent examples of power-law distributed data include natural language (Zipf’s law [40]) and scale-free networks such as social and …

PROFORMA: Proactive Forensics with Message Analytics
A Gupta, S Dasgupta, A Bagchi – IEEE Security & Privacy, 2017 – ieeexplore.ieee.org
… The social con- text graph is then stored in the heterogeneous data store that contains a component for efficient storage of graphs … We contend that although full-scale natural language processing (NLP) that identifies the phrasal structures and named entities of text is a bet- ter …

Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection
L Song, RYK Lau, RCW Kwok, K Mirkovski… – Electronic Commerce …, 2017 – Springer
… Previous studies use different features (eg, user-, text, graph-, and social network-related attributes) and classification algorithms (eg, Naïve Bayesian and Bayesian Network) to design frameworks for detecting and reducing social spam on many social media platforms (eg …

Vision-Language Fusion for Object Recognition.
SR Shiang, S Rosenthal, A Gershman, JG Carbonell… – AAAI, 2017 – aaai.org
… In addition, robots interacting with hu- mans via natural language would also need such an ability to integrate what has been seen and what has been told … We note that speech recognition and natural language parsing are outside the scope of this paper …

Automating the Generation of Enticing Text Content for High-Interaction Honeyfiles
B Whitham – Proceedings of the 50th Hawaii …, 2017 – scholarspace.manoa.hawaii.edu
… his colleagues developed an automatic computer-science paper generator, called SCIGen [3]. SCIGen constructed entire academic publications, including text, graphs, figures, and … Section 4 of this paper presents four new designs based on Natural Language Processing (NLP …

Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT …
D Kim, H Lee, J Kwak – Research Policy, 2017 – Elsevier
Skip to main content …

Deep keyphrase generation
R Meng, S Zhao, S Han, D He, P Brusilovsky… – arXiv preprint arXiv …, 2017 – arxiv.org
… As for unsupervised approaches, primary ideas include finding the central nodes in text graph (Mihalcea and Tarau, 2004; Grineva et al., 2009), detecting representative phrases from topi- cal clusters (Liu et al., 2009, 2010), and so on …

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review
MS Rani, S Sumathy – International Journal of Engineering & …, 2017 – w.sciencepubco.com
… Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches …

Unified framework for control of machine learning tasks towards effective and efficient processing of big data
H Liu, A Gegov, M Cocea – Data science and big data: An environment of …, 2017 – Springer
… ordinal [17]. In machine learning and statistics, data types can be simply divided into two categories: discrete and continuous. On the other hand, data can be represented in different forms, eg text, graph and tables. All the differences …

Text summarization techniques: A brief survey
M Allahyari, S Pouriyeh, M Assefi, S Safaei… – arXiv preprint arXiv …, 2017 – arxiv.org
… a new way. In other words, they interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. Even though …

Second-order orthant-based methods with enriched Hessian information for sparse $ $\ell _1 $ $ ? 1-optimization
JC De Los Reyes, E Loayza, P Merino – Computational Optimization and …, 2017 – Springer
… Let us denote its limit by \({\bar{x}}\). Since \({{\widetilde{\nabla }}} \varphi (x^k) \in \nabla f(x^k) + \partial (\beta {\Vert \cdot \Vert }_1) (x^k)\) we obtain that the pair \((x^k,{{\widetilde{\nabla }}} \varphi (x^k) ) \in \text {Graph} ( \nabla f + \partial (\beta {\Vert \cdot \Vert }_1) )\) …

Multi-document summarization based on sentence cluster using non-negative matrix factorization
L Yang, X Cai, S Pan, H Dai… – Journal of Intelligent & …, 2017 – content.iospress.com
… The ClusterCMRW (Cluster-based Conditional Markov Random Walk) model incorporates the cluster-level information into the text graph and manipulates clusters and sentences equally, the Cluster-HITS model treats clusters and sentences as hubs and authorities in the HITS …

Illustrate It! An Arabic Multimedia Text-to-Picture m-Learning System
AG Karkar, JM Alja’am, A Mahmood – IEEE Access, 2017 – ieeexplore.ieee.org
… Jain et al. [18] proposed a Hindi natural language processing called Vishit. It visualizes the text to help the communication between cultures that use different languages at university … Algorithm 3 Compute Graph Intersection Score Input: Picture graph Gp, text graph GE …

Joint Image-Text Topic Detection and Tracking for Analyzing Social and Political News Events
W Li – 2017 – search.proquest.com
… [CZL14] detects topics within one multimodal graph, which is obtained by merging one text graph and another … The MT-AOG model strikes a balance between the syntactic representation in natural language processing (too complex to compute) and the simplistic bag-of-words …

MIKE: Keyphrase Extraction by Integrating Multidimensional Information
Y Zhang, Y Chang, X Liu, SD Gollapalli, X Li… – Proceedings of the 2017 …, 2017 – dl.acm.org
… Since keyphrases can provide a high-level topic descrip- tion of a document, they are useful for a wide range of natural language processing tasks such as text summarization [22], infor- mation retrieval [25] and question answering [44]. However, the …

Graph-based Interactive Bibliographic Information Retrieval Systems
Y Zhu – 2017 – search.proquest.com
… We propose form-, natural language-, and visual graph-based systems that allow users to formulate bibliographic queries in a variety of ways … In the natural language-based system, users formulate queries using a natural language …

Dimensions of Variation in Written Chinese
ZS Zhang – 2017 – books.google.com
Page 1. ROUTLEDGE STUDIES IN CHINESE LINGUISTICS T Dimensions of Voriction in Written Chinese Zheng-sheng Zhong Page 2. Dimensions of Variation in Written Chinese Dimensions of Variation in Written Chinese uses …

Modeling and mining business process variants in cloud environments
K Yongsiriwit – 2017 – theses.fr
… Figure 1.3: Process data heterogeneity natural language have mostly implied semantic heterogeneity in BP models. In fact, an organization’s branches usually define their own BP models with their preferred modeling language and element’s labels as they like …

Knowledge Discovery From Massive Data Streams
SK Narang, S Kumar, V Verma – Web Semantics for Textual and …, 2017 – books.google.com
… But web data is generally not structured like data- bases; rather it is semi-structured data such as HTML documents or unstructured data such as text, graphs, images and video. Therefore, the extraction of information and interesting patterns out of the Web is a complex task …

Spectral Graph Convolutional Networks for Part-of-Speech Tagging
S Demirel – 2017 – kola.opus.hbz-nrw.de
… Following example extracted from the Natural Language Toolkit (NLTK)4 demonstrates the structure of the tagged corpus, which will become … an image pixel plus intensity and a PoS with corresponding feature vectors, while the generated text graph, depicting neighborhood …

Joint image-text news topic detection and tracking by multimodal topic and-or graph
W Li, J Joo, H Qi, SC Zhu – IEEE Transactions on Multimedia, 2017 – ieeexplore.ieee.org
… The MT-AOG model strikes a balance between the syntactic representation in natural language processing (too complex to compute) and the simplistic bag-of-words … [38] detects topics within one multimodal graph, which is obtained by merging one text graph and another visual …

Extracting Future Crime Indicators from Social Media
T Delavallade, P Bertrand, V Thouvenot – Using Open Data to Detect …, 2017 – Springer
… Identifying texts matching these patterns can be done with neural-network-based natural language processing algorithms (Lai et al … As can be ascertained from the appearance of the text graph on the left, this visualization tool is able to display very large graphs (more than …

Feature Extraction and Duplicate Detection for Text Mining: A Survey
RS Ramya, KR Venugopal… – Global Journal of …, 2017 – computerresearch.org
… Mousavi et al., [34] have formulated a weighted graph depiction of text, called Text Graphs that further captures grammar which serve as semantic dealings between words that are in textual … Keyphrase extraction is a basic research in text mining and natural language processing …

Computational Data Sciences and the Regulation of Banking and Financial Services
S O’Halloran, M Dumas, S Maskey, G McAllister… – From Social Data Mining …, 2017 – Springer
… Keywords. Big data Natural language process Machine learning Topical modeling Political economics Financial market regulation. Download fulltext PDF. 1 Introduction … 3.1 Data Representation Using Natural Language Processing …

Granular computing based machine learning: a big data processing approach
H Liu, M Cocea – 2017 – books.google.com
… Regression Trees Computational Intelligence Genetic Algorithm Information Entropy Based Rule Generation K Nearest Neighbours Naive Bayes Natural Language Processing Probabilistic … On the other hand, data can be represented in different forms, eg text, graph and tables …

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams
Y Wu, Z Chen, G Sun, X Xie, N Cao… – IEEE transactions on …, 2017 – ieeexplore.ieee.org
Page 1. 1077-2626 (c) 2017 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …

Granular Computing Based Machine Learning
H Liu, M Cocea – Springer
… Computational Intelligence GA Genetic Algorithm IEBRG Information Entropy Based Rule Generation KNN K Nearest Neighbours NB Naive Bayes NLP Natural Language Processing PNN … On the other hand, data can be represented in different forms, eg text, graph and tables …

Business Intelligence and Analytics: Big Systems for Big Data
H Herodotou – Analytics, Innovation, and Excellence-Driven …, 2017 – Springer
The amount of data collected by modern industrial, government, and academic organizations has been increasing exponentially and will continue to grow at an accelerating rate for the foreseeable future.

Using social media for air pollution detection-the case of Eastern China Smog
Y Shi, H Gao – 2017 – lup.lub.lu.se
Page 1. I Department of Informatics Using Social Media for air pollution detection The case of Eastern China Smog Master thesis 15 HEC, course INFM10 in Information Systems Presented in June, 2017 Authors: Han Gao Yu Shi Supervisor: Zafeiropoulou Styliani …

GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques
T Falke, I Gurevych – … on Empirical Methods in Natural Language …, 2017 – aclweb.org
… 1 ? 2 1 1 1 1 ? 1 ? 1 Figure 5: Data structure to capture different types of text graphs in UML-style class notation. tem supports both labeled and unlabeled as well as directed and undirected graphs. As examples, we created two graph generation modules for our framework …

Benchmarking Big Data Systems: A Review
R Han, LK John, J Zhan – IEEE Transactions on Services …, 2017 – ieeexplore.ieee.org
Page 1. 1939-1374 (c) 2017 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …

Cognitive Computing: Where Big Data Is Driving Us
AP Appel, H Candello, FL Gandour – Handbook of Big Data Technologies, 2017 – Springer
… Also, much of human communication, whether it is in natural-language text, speech, or images, is unstructured … Most of data produced in the last decade, around 80%, is unstructured data composed by video, images, long text, graph data and so on which do not fit in relational …

Structured prediction and generative modeling using neural networks
K Kastner – 2017 – papyrus.bib.umontreal.ca
… of learning algorithms. There are many other ways to represent data which can 2 Page 16. capture additional structure in data such as images, text, graphs, audio, or video that certain algorithms can use for better learning. Before discussing learning …

Intelligent Tutoring System Effects on the Learning Process
ATQ Al-Aqbi – 2017 – rave.ohiolink.edu
Page 1. Intelligent Tutoring System Effects on the Learning Process A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Engineering By ALI TALIB QASIM AL-AQBI B.Sc., University of Technology, 2010 …

Demystifying Big Data and Machine Learning for Healthcare
P Natarajan, JC Frenzel, DH Smaltz – 2017 – books.google.com
Page 1. Demystifying Big Data and Machine Learning for HealthCare Page 2. Demystifying Big Data and Machine Learning for Healthcare Page 3. Taylor & Francis Taylor & Francis Group http://taylorandfrancis.com Page 4. Advance …

Learning data mining with python
R Layton – 2017 – books.google.com
… Natural language processing and part-of-speech tagging Discovering Accounts to Follow Using Graph Mining More complex algorithms NetworkX Beating CAPTCHAs with Neural Networks Better (worse?) CAPTCHAs Deeper networks Reinforcement learning Authorship …

Identification & visualization of patient information elements to support chronic iIlness care: a scoping review and pilot study
V Kinch – 2017 – dspace.library.uvic.ca
Page 1. Identification & Visualization of Patient Information Elements to Support Chronic Illness Care: A Scoping Review and Pilot Study by Vanessa Kinch BScN, University of Northern British Columbia, 2005 A Thesis Submitted …

Being Skilled: The socializations of learning to read
S McNaughton – 2017 – books.google.com
… Graph 1 High and low progress ‘code emphasis’ readers Source: Cohen (1975) Page 20. Graph 2 High and low progress ‘natural language’ readers Source: Clay (1966) Figure 1 Reading behaviour of first-year children in two programmes Without further data this observation …

From dual coding to multiple coding: Effects of multiple symbolic representations for mathematical understanding
N Ott – 2017 – publikationen.sulb.uni-saarland.de
Page 1. From Dual Coding to Multiple Coding: Effects of Multiple Symbolic Representations for Mathematical Understanding Dissertation zur Erlangung des akademischen Grades eines Doktors der Philosophie der Fakultät HW Bereich Empirische Humanwissenschaften …