Notes:
A text graph is a visual representation of the relationships between words and concepts in a text item. It can help to show how different ideas in the text are connected and can be useful for analyzing and summarizing the content of the text. There are various ways to create text graphs, including manually drawing them or using software tools that automatically generate the graph based on the text input. Text graphs can be useful for a variety of purposes, such as information visualization, text mining, and natural language processing.
Text graphs can be used in natural language processing (NLP) for a variety of purposes. For example, they can be used to represent the syntactic structure of a sentence or the semantic relationships between words in a text. This can be useful for tasks such as part-of-speech tagging, named entity recognition, and dependency parsing, which involve analyzing the structure and meaning of language.
Text graphs can also be used to represent the relationships between different documents in a collection, such as a corpus of texts. This can be useful for tasks such as document classification, clustering, and summarization, which involve analyzing the content and relationships between multiple texts.
NLG pipeline architecture refers to the set of steps or stages involved in generating natural language text from structured data using natural language generation (NLG) systems. The goal of an NLG system is to automatically produce human-like text that communicates information or ideas effectively.
Text graphs can be used at various stages in an NLG pipeline, depending on the specific requirements and design of the system. For example, they might be used to represent the input data that the NLG system is processing, such as a database of information or a set of structured data. They might also be used to represent the intermediate steps in the NLG process, such as the syntactic or semantic structure of the generated text.
Here are some examples of how text graphs might be used in an NLG pipeline:
- Input representation: Text graphs could be used to represent the input data that an NLG system is processing. For example, a text graph might represent a database of information about a set of products, with nodes representing different products and edges representing relationships between them (e.g., “Product A is similar to Product B”).
- Planning: Text graphs could be used to represent the intermediate steps in the NLG process, such as the desired structure or content of the generated text. For example, a text graph might represent the overall structure of a document, with nodes representing different sections or ideas and edges representing relationships between them (e.g., “Section A introduces the main topic, while Section B provides details”).
- Text generation: Text graphs could be used to represent the generated text itself, such as the syntactic or semantic structure of the text. For example, a text graph might represent the syntax of a sentence, with nodes representing different words and edges representing syntactic dependencies between them (e.g., “The subject of the sentence is ‘the cat,’ and the verb is ‘sat'”).
Wikipedia:
References:
See also:
Dynamic Topic Modeling | Sentence Summarization
Graph convolutional networks for text classification
L Yao, C Mao, Y Luo – Proceedings of the AAAI Conference on …, 2019 – wvvw.aaai.org
… Abstract Text classification is an important and classical problem in natural language processing … We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the cor- pus …
Improving natural language inference using external knowledge in the science questions domain
X Wang, P Kapanipathi, R Musa, M Yu… – Proceedings of the …, 2019 – wvvw.aaai.org
… Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering … We present the results of applying our techniques on text, graph, and text-and-graph based models; and discuss the …
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
K Bijari, H Zare, E Kebriaei, H Veisi – arXiv preprint arXiv:1902.10247, 2019 – arxiv.org
… 2.2. Motivation In natural language processing, bag-of-word representation is one of the most … ically, in the proposed method as a novel feature learning algorithm, node2vec, is used to reveal the innate and essential information of a text graph (Grover & Leskovec, 2016) …
Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search
J Liu, ZJ Zha, R Hong, M Wang, Y Zhang – Proceedings of the 27th ACM …, 2019 – dl.acm.org
… The newly emerging text-based person search task aims at retriev- ing the target pedestrian by a query in natural language with fine … Specifically, the A-GANet consists of an image graph attention net- work, a text graph attention network and an adversarial learning module …
Text Summarization Using WordNet Graph Based Sentence Ranking
A Jain, S Vij, DK Tayal – Proceedings of 2nd International Conference on …, 2019 – Springer
… It is a key research area in the field of computational science and natural language processing … 3. [2]. A semi-supervised learning based method is deployed for creating the text graph for summarization. Demonstrates how to generate a hypergraph for summarization …
An empirical comparison of distance/similarity measures for Natural Language Processing using Text Graph Convolutional Networks
DKS Magalhaes, ATR Pozo, R Santana – bracis2019.ufba.br
Text Classification is one of the tasks of Natural Language Processing (NLP). In this area, Graph Convolutional Networks (GCN) has achieved values higher than CNN’s and other related models. For GCN, the term frequency–inverse document frequency (TF-IDF) defines …
Large Scale Hierarchical Natural Language Text Classification using Deep Graph-CNN
N Rohini, N Subramanian, C Sunitha, A Ganesh – ijresm.com
… Large Scale Hierarchical Natural Language Text … They build a large and heterogeneous text graph which contains word nodes and document nodes so that global word co-occurrence can be explicitly modeled and graph convolution can be easily adapted …
Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts
HJ Yoon, J Gounley, MT Young… – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
… In this paper, we consider the latest graph-based convolutional neural network technique, the Text Graph Con- volutional Network (Text GCN), in the context of performing classification tasks on free-form natural language texts …
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
D Ustalov, S Somasundaran, P Jansen… – … for Natural Language …, 2019 – aclweb.org
… on problems related to either Graph Theory or graph- based algorithms applied to Natural Language Processing, Social … 52 Layerwise Relevance Visualization in Convolutional Text Graph Classifiers Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt and …
Graph-Based Semi-Supervised Learning for Natural Language Understanding
Z Qiu, E Cho, X Ma, W Campbell – … -Based Methods for Natural Language …, 2019 – aclweb.org
… Natural language understanding (NLU) technol- ogy is an important component for a dialog sys- tem and is commonly used in voice … We propose two transductive graph models for semi-supervised learning NLU tasks, Text Graph Convolutional Network (TGCN) and Text Graph …
Exploring Multi-label Classification Using Text Graph Convolutional Networks on the NTCIR-13 MedWeb Dataset
S TAO, T SAKAI – db-event.jpn.org
… Abstract The NTCIR-13 Medical Natural Language Processing for Web Document (MedWeb) task requires participant systems to perform multi … While a recent study showed that a method based on Text Graph Convolutional Networks outperforms previous approaches in text …
Towards a graph model application for automatic text processing in data management
EG Grigoryeva, LA Kochetova… – IOP Conference …, 2019 – iopscience.iop.org
… Within “bag-of-words” model, the authors have found that the primary Zipf’s law, which states that given some corpus of natural language utterances, the … Therefore, we set the task of finding the characteristics of a text graph that would not be invariant, regardless of mixing …
Dependency-based Text Graphs for Keyphrase and Summary Extraction with Applications to Interactive Content Retrieval
P Tarau, E Blanco – arXiv preprint arXiv:1909.09742, 2019 – arxiv.org
… together they will take significant forward steps in natural language understanding. This has motivated us to experiment with a uni- fied keyphrase extraction and summarization al- gorithm. The main novelty is to leverage depen- dency trees to generate text graphs instead of co …
Text Level Graph Neural Network for Text Classification
L Huang, D Ma, S Li, X Zhang, H WANG – arXiv preprint arXiv:1910.02356, 2019 – arxiv.org
… 1 Introduction Text classification is a fundamental problem of natural language processing (NLP), which has lots of applications like SPAM detection, news filter- ing, and so on (Jindal and Liu, 2007; Aggarwal and Zhai, 2012) … 2.1 Building Text Graph …
Using Graphs for Word Embedding with Enhanced Semantic Relations
M Zuckerman, M Last – … on Graph-Based Methods for Natural Language …, 2019 – aclweb.org
… Page 2. 33 2.1 Text Representation One of the challenges in natural language process- ing is how to represent a term … Graph representation is not new in the world of text processing (Schenker et al., 2005; Son- awane and Kulkarni, 2014) …
Short Text Classification using Graph Convolutional Network
K Tayal, N Rao, S Agrawal, K Subbian – assets.amazon.science
… Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce … The method uses side-information present in the dataset to construct a graph and then learn a Short Text Graph Convolutional Networks (ST-GCN) …
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
P Kapanipathi, V Thost, SS Patel, S Whitehead… – arXiv preprint arXiv …, 2019 – arxiv.org
… Utilizing external knowledge has shown improvement in performance on many natural language processing (NLP) tasks (Huang et al … compatibility with various existing models, we compared all text-based models described above (Section 4.3) to a com- bined text+graph model …
Unsupervised Text Generation from Structured Data
M Schmitt, S Sharifzadeh, V Tresp… – arXiv preprint arXiv …, 2019 – arxiv.org
… Thus, there is a need for methods such as automatic natural language summarization that support non-experts working with KGs … Bidirectionality. Following Lample et al. (2018b), we train our system for both directions text ? graph, sharing encoder and decoder …
Knowledge-aware Textual Entailment with Graph Attention Network
D Chen, Y Li, M Yang, HT Zheng, Y Shen – Proceedings of the 28th ACM …, 2019 – dl.acm.org
… has been attracting a lot of interest and it poses significant issues in front of systems aimed at natural language understand- ing … We fur- ther propose a text-graph interaction mechanism for neural based entailment matching learning, which endows the redundancy and noise …
Graph-Based Semantic Learning, Representation and Growth from Text: A Systematic Review
I Ali, A Melton – 2019 IEEE 13th International Conference on …, 2019 – ieeexplore.ieee.org
… It is obvious that for the purpose of communication, the semantics (meanings) in natural language is not limited to our linguistic skills, but rather it is used for and by our mental cognitive skills such as perception … C. Cognitive Models of Text Graphs and Their Applications …
Recursive Graphical Neural Networks for Text Classification
W Li, S Li, S Ma, Y He, D Chen, X Sun – arXiv preprint arXiv:1909.08166, 2019 – arxiv.org
… Abstract The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models … In this paper, we propose a recursive graphical neural net- works model to encode the text graph constructed from the word co-occurrence information …
RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis
J Chen, H Hou, J Gao, Y Ji, T Bai – International Conference on …, 2019 – Springer
… After pooling operations, the hidden feature vectors of nodes is denoted as \(H^{(p)}\in \mathbb {R}^{F}\), which is the representation of the text graph … In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp …
An Exploratory Study on Automatic Architectural Change Analysis Using Natural Language Processing Techniques
AKMBR Kevin, A Schneider – researchgate.net
… VI. ARCHITECTURAL CHANGE MESSAGE DETECTION A text graph [14] is effective for numerous applications involving information extraction from natural language text documents. In our work, we represent the key-terms …
An Exploratory Study on Automatic Architectural Change Analysis Using Natural Language Processing Techniques
AK Mondal, B Roy, KA Schneider – 2019 19th International Working … – ieeexplore.ieee.org
… VI. ARCHITECTURAL CHANGE MESSAGE DETECTION A text graph [14] is effective for numerous applications involving information extraction from natural language text documents. In our work, we represent the key-terms …
Deploying Natural Language Processing to Extract Key Product Features of Crowdfunding Campaigns: The Case of 3D Printing Technologies on Kickstarter
N Chaichi, T Anderson – 2019 Portland International …, 2019 – ieeexplore.ieee.org
Page 1. Deploying Natural Language Processing to Extract … TextranN is derived from the Google PageranN algorithm which utilized for Neyword extraction and text summarization [15]. In the TextranN approach, the text graph consists of vertices and edges …
Text Classification Based On Fuzzy Radial Basis Function
Z Ali – Iraqi Journal for Computers and Informatics, 2019 – ijci.uoitc.edu.iq
… It’s one of the important tasks of Natural Language Processing (NLP) with wide applications such as spam detection, sentiment analysis and topic labeling [2]. There are two ways for TC manually and automatically … used Text Graph Convolution Networks (Text GCN) for TC …
GCNDA: Graph Convolutional Networks with Dual Attention Mechanisms for Aspect Based Sentiment Analysis
J Chen, H Hou, J Gao, Y Ji, T Bai, Y Jing – International Conference on …, 2019 – Springer
… The output of the GCNs is forwarded to the pooling operations layers, which is the element operation on hidden vectors to get representation of the text graph … In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1947–1957 …
Measuring The Complexity Of Natural Language Requirements In Industrial Control Systems
K Rajkovi? – 2019 – diva-portal.org
… and its attributes. They can often contain text, graphs, figures and diagrams. However, they are still mostly written in Natural Language (NL) in industry, which is also a convenient way of representing them. With the increase …
Graph-Based Attention Networks for Aspect Level Sentiment Analysis
J Chen, H Hou, Y Ji, J Gao, T Bai – 2019 IEEE 31st International …, 2019 – ieeexplore.ieee.org
… First, we build the text graph based on syntactic dependency relation as shown in Figure 3. As can be seen from the figure, there are … In contrast, the recurrent neural networks such as LSTM [38] and GRU [39] have been proven to be effective in natural language learning tasks …
Text Summarization in Indian Languages: A Critical Review
N Baruah, SK Sarma… – 2019 Second International …, 2019 – ieeexplore.ieee.org
… Keywords—Indian language, natural language processing, text summarization … In the field of text summarization, Natural Language Processing (NLP) is designed to develop interactions between natural languages and computer …
Graph-based language grounding
M Bajaj – 2019 – open.library.ubc.ca
Search. The University of British Columbia. UBC – A Place of Mind. The University of British Columbia. UBC Search UBC Search. Library. Library Home; Search Collections: Search; General (Summon); Books & Media (Catalogue …
Suggestion miner at semeval-2019 task 9: suggestion detection in online forum using word graph
U Ahmed, H Liaquat, L Ahmed… – Proceedings of the 13th …, 2019 – aclweb.org
… 4.2 Maximum Common Sub Graph We used three variations of maximum common sub graph metric to find similarity between text graph and class graph … In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Process- ing, pages 2159–2167 …
Dual-Attention Graph Convolutional Network
X Zhang, T Zhang, W Zhao, Z Cui, J Yang – arXiv preprint arXiv …, 2019 – arxiv.org
… Keywords: dual-attention · graph convolutional networks · text classi- fication. 1 Introduction Text classification is an active research field of natural language processing and multimedia, and has attracted increasing attention in recent years … ?1 ?2 Text graph Fig …
Graph Star Net for Generalized Multi-Task Learning
L Haonan, SH Huang, T Ye, G Xiuyan – arXiv preprint arXiv:1906.12330, 2019 – arxiv.org
… Page 5. not a graph task; it is usually treated as a natural language processing task (NLP) … Different from previous text graph-based models[23, 27], here we conduct the text classification as graph classification instead of node classifications …
Topological Representation of Text for Entailment
K Savle – 2019 – search.proquest.com
… Page 22. 11 as machine learning features. However, finding the corresponding natural language mechanisms … 2.5.1 Graph CNN for Text Classification [Yao et al., (2018)] constructed a single text graph which is based on word-cooccurrence and document word relation …
Constructive and object-oriented modeling text for detection of text borrowings
OS Kuropiatnyk – System technologies, 2019 – journals.nmetau.edu.ua
Page 1. «» 4 (123) 2019 «System technologies» ISSN 1562-9945 34 DOI 10.34185/1562-9945-4-123-2019-04 ??? 510.25+004.415.2+004.912 ?.?.
Goal-based Ontology Creation for Natural Language Querying in SAP-ERP Platform
N Gantayat, D Saha, J Sen, S Mani – Proceedings of the ACM India Joint …, 2019 – dl.acm.org
… Natural Language Querying in SAP-ERP Platform … It enables business users to ask questions in natural language without needing to have any pro- gramming knowledge (such as ABAP or SQL) and knowledge about the data representation mechanisms (such as data schema) …
Improved knowledge graph embedding using background taxonomic information
B Fatemi, S Ravanbakhsh, D Poole – … of the AAAI Conference on Artificial …, 2019 – aaai.org
… A common approach to apply machine learning to sym- bolic data, such as text, graph and entity-relationships, is through embeddings … vectorize words, sentences and para- graphs using context information, are widely used in a va- riety of natural language processing tasks …
Learning Dynamic Context Graphs for Predicting Social Events
S Deng, H Rangwala, Y Ning – Proceedings of the 25th ACM SIGKDD …, 2019 – dl.acm.org
… New graph models (1b) ignore the temporal evolution of graphs in a sequence of inputs; Dynamic con- text graph models (1c … For natural language processing tasks, GCN has been successfully applied in semantic role labeling [14] to encode syntactic structure of sentences, text …
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
Z Jiang, Z Gao, G He, Y Kang, C Sun, Q Zhang… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation Zhuoren Jiang1?, Zhe Gao2?, Guoxiu He3, Yangyang Kang2, Changlong Sun2 …
Graphflow: Exploiting conversation flow with graph neural networks for conversational machine comprehension
Y Chen, L Wu, MJ Zaki – arXiv preprint arXiv:1908.00059, 2019 – arxiv.org
Page 1. GRAPHFLOW: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension Yu Chen? Rensselaer Polytechnic Institute cheny39@rpi.edu Lingfei Wu? IBM Research lwu@email.wm.edu …
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
R Schwarzenberg, M Hübner, D Harbecke, C Alt… – arXiv preprint arXiv …, 2019 – arxiv.org
… 6 Conclusions We presented layerwise relevance visualization in convolutional text graph classifiers … 2019. Scispacy: Fast and robust models for biomedical natural language processing. Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016 …
Graph Generation with a Focusing Lexicon
MC Shekar, JA Cottam – … Conference on Big Data (Big Data), 2019 – ieeexplore.ieee.org
… The text-graph generation is an ensemble model of state of the art semantic role labeling models with our key-phrase extraction … and P. Tarau, “Textrank: Bringing order into text,” in Proceedings of the 2004 conference on empirical methods in natural language processing, 2004 …
Building Causal Graphs from Medical Literature and Electronic Medical Records
G Nordon, G Koren, V Shalev, B Kimelfeld… – Proceedings of the …, 2019 – wvvw.aaai.org
… While the text graph is based on causal connections implied by natural language in the text, the EMR graph is undirected and contains only correlations. We use a lack of correla- tion in the EMR data as a criterion for pruning the dense text-derived graph …
Automatic Labeling of Topic Models Using Graph-Based Ranking
D He, M Wang, AM Khattak, L Zhang, W Gao – IEEE Access, 2019 – ieeexplore.ieee.org
… I. INTRODUCTION The topic model is a very important technique in natural language processing, such as information retrieval and text mining, and the results of topic discovery are usually … (3) In the final stage, candidate sentences can be used as vertices to create a text graph …
Matching Article Pairs with Graphical Decomposition and Convolutions
B Liu, D Niu, H Wei, J Lin, Y He, K Lai… – Proceedings of the 57th …, 2019 – aclweb.org
… ural language matching. 1 Introduction Identifying the relationship between a pair of arti- cles is an essential natural language understand- ing task, which is critical to news systems and search engines. For example, a news …
An improved TextRank keywords extraction algorithm
S Pan, Z Li, J Dai – Proceedings of the ACM Turing Celebration …, 2019 – dl.acm.org
… KEYWORDS Keywords extraction, TF-IDF algorithm, TextRank algorithm, Average information entropy, Natural language processing … These text units are used as nodes, and the similarity between the nodes is used as edges, and then a text graph is formed …
Sos textvis: An extended survey of surveys on text visualization
M Alharbi, RS Laramee – Computers, 2019 – mdpi.com
… This volume of digital text data makes understanding and analyzing it extremely challenging. Text documents by their nature bring many challenges such as high dimensionality, irregularity, and uncertainty inherent in natural language …
Graph-based food ingredient detection
B Ghotbi – 2019 – open.library.ubc.ca
Search. The University of British Columbia. UBC – A Place of Mind. The University of British Columbia. UBC Search UBC Search. Library. Library Home; Search Collections: Search; General (Summon); Books & Media (Catalogue …
Graph Convolutional Networks with Structural Attention Model for Aspect Based Sentiment Analysis
J Chen, H Hou, Y Ji, J Gao – 2019 International Joint …, 2019 – ieeexplore.ieee.org
… In Natural Language Processing (NLP), the text graph has been widely used in keywords extraction [18]–[20], textual inference [21], document summarization [22], text classifica- tion [23] and so on. These applications illustrate the necessity of regarding the text as a graph …
DSS: A Data Science Suite
RS Pereira, F Porto – researchgate.net
… DIVE[Hu et al. ], for instance, from MIT features data visualization, RapidMiner[Cha ] provides natural language processing tools and Gephi[Bastian et al … Comparison of PDF files for similarity • Finding themes contained in text • Sentiment Analysis in text • Graph data Analysis …
Semantic Similarity Approach between two Sentences
Y Bounab, A Zitouni, M Oussalah, AC Megherbi… – researchgate.net
… Needless to say that designing improved natural language processing algorithms and software would benefit several information retrieval, machine translation, text … An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis …
Medical Health Posts Summarization Using Lesk Algorithm
VL Mane, A Abhale, S Khandelwal – academia.edu
… Rashmi Mishra et al., classified summarization methods into categories like statistical, natural language processing, machine learning, and hybrid … Murali Krishna VV Ravinuthala, Satyananda Reddy Chproposed a technique called thematic text graph which is nothing but …
Understanding and Generating Multi-Sentence Texts
RK Kedziorski – 2019 – digital.lib.washington.edu
… 2.1 Natural Language Understanding A great deal of work has been done in natural language understanding, but work that focuses on … Page 26. 2.2 Natural Language Generation The current work explores two tactics for generating longer natural language texts. On the one …
Automatic keyphrase extraction using word embeddings
Y Zhang, H Liu, S Wang, WH Ip, W Fan, C Xiao – Soft Computing, 2019 – Springer
… In this paper, we first design a heterogeneous text graph embedding learning model to deeply incorporate the local context information of … 2018), have been proposed, they are mainly designed for general natural language processing (NLP) tasks such as text classification or …
Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching
J Hu, S Qian, Q Fang, C Xu – Proceedings of the 27th ACM International …, 2019 – dl.acm.org
… Text GCN [30] builds a single text graph for a corpus based on word co-occurrence and document word relations, then learns a Text Graph Convolutional Network for the corpus to learn word and document embeddings. Peng et al …
Unconventional Methods in Voynich Manuscript Analysis
I Zelinka, O Zmeskal, L Windsor, Z Cai – MENDEL, 2019 – ib-b2b.test.infv.eu
… text has been taken as (b) Figure 5: The principle of the text-graph conversion. 5.3 The fractal analysis … The veracity of the Voynich Manuscript has been hotly debated, ie, whether it is a randomly generated nonsense strings, or whether it is written in some natural language …
Keyword Extraction with Character-Level Convolutional Neural Tensor Networks
ZL Lin, CJ Wang – Pacific-Asia Conference on Knowledge Discovery and …, 2019 – Springer
… Such keywords or keyphrases provide rich information about the content and help improve the performance of natural language processing (NLP) and … We apply TextRank to a directed, weighted text graph, and use co-occurrence links to express the relations between two words …
NamedKeys: Unsupervised Keyphrase Extraction for Biomedical Documents
Z Gero, JC Ho – Proceedings of the 10th ACM International Conference …, 2019 – dl.acm.org
… Extracting keyphrases from doc- uments is of paramount importance for natural language processing (NLP) tasks such as text summarization [2, 37], text … that accounts for the local context of a text unit (vertex) and the information recursively drawn from the entire text (graph) …
A deep extraction model for an unseen keyphrase detection
AG Zahedi, M Zahedi, M Fateh – Soft Computing, 2019 – Springer
… 2015) have produced decent qualities in the field of natural language processing tasks and are often represented as a sequence of input–output in modeling … For unsupervised approaches, early ideas are involved with finding central nodes in the text graph (Grineva et al …
Entailment and Spectral Clustering based Single and Multiple Document Summarization
A Gupta, M Kaur, A Bajaj, A Khanna – International Journal of …, 2019 – mecs-press.net
… Moreover, the proposed methods have exhibited faster execution times. Index Terms—Automatic Summarization, Textual Entailment, Spectral Clustering, Information Retrieval, Extractive Techniques, Natural Language Processing. I. INTRODUCTION …
BPMN-E2: a BPMN extension for an enhanced workflow description
M Ramos-Merino, JM Santos-Gago… – Software & Systems …, 2019 – Springer
… The proposed extension supports the representation of information commonly used by experts in the hazard analysis and critical control points domain, usually expressed in natural language, in a machine-understandable fashion …
Keywords Extraction from Arabic Documents Using Centrality Measures
W Al Etaiwi, AA Awajan… – 2019 Sixth International …, 2019 – ieeexplore.ieee.org
… Keywords extraction refers to the task of automatically identifying a set of words that describes the main ideas of the document [1]. Keywords extraction is an important task that can be used to solve several Natural Language Processing (NLP … In text graphs, degree centrality of a …
sCAKE: semantic connectivity aware keyword extraction
S Duari, V Bhatnagar – Information Sciences, 2019 – Elsevier
… Keywords. Automatic keyword extraction. Text graph. Semantic connectivity. Parameterless. Language agnostic. 1. Introduction … Graph-based approaches denote candidate keywords as nodes and the relationship between two nodes as an edge …
Tradeoffs Between Embeddings in Different Models of the Hyperbolic Space
E Lou, S Zhang, I Gaur – shawnbzhang.github.io
… embeddings significantly improve the performance of various tasks that benefit from hierarchical organization (3). One such category of tasks is natural language processing, as the text graphs better fit a curved, hyperbolic space than the standard Euclidean space (2) due to the …
Applicability of Text-representing Centroids for Thai Language Documents
S Nualnim, N Romyen, M Sodanil – … Conference on Natural Language …, 2019 – dl.acm.org
… And finally, the text graph structure is constructed according to Figure 2. Figure 2. The process of graph structure representing text. The construction of a text graph structure is described as follows Input: Initialize the node set * +, the edge set * + and …
Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation
C Wang, L Marini, CL Chin, N Vance… – 2019 15th …, 2019 – ieeexplore.ieee.org
… SMILE provides a user-friendly web interface, through which researchers can perform a wide spectrum of research tasks, ranging from social media data collection, natural language processing, text classification, social network analysis, and generating human readable outputs …
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
M Shi, Y Tang, X Zhu, J Liu – arXiv preprint arXiv:1912.11755, 2019 – arxiv.org
… relationships between nodes, DeepWalk [8] performs a random walk process over the whole graph to generate a collection of fixed-length node sequences similar to the natural language sentences … [3] proposed the Text GCN for text classification, where the text graph is built …
Extracting Keyphrases from Research Papers Using Word Embeddings
W Fan, H Liu, S Wang, Y Zhang, Y Chang – Pacific-Asia Conference on …, 2019 – Springer
… provide a high-level topic description of a document, they are very useful for a wide range of natural language processing (NLP … In this paper, we first design a heterogeneous text graph representation learning approach to deeply incorporate the local context information of the …
Information geometry enhanced fuzzy deep belief networks for sentiment classification
M Wang, ZH Ning, T Li, CB Xiao – International Journal of Machine …, 2019 – Springer
… reviews. Efficient sentiment analysis over such reviews using deep learning techniques has become an emerging research topic, which has attracted more and more attention from the natural language processing community …
Novel Representations, Regularization & Distances for Text Classification
G Manolopoulos – 2019 – lix.polytechnique.fr
… ACM. 2015, pp. 1473–1479. 2. Konstantinos Skianis, François Rousseau, and Michalis Vazirgiannis. “Regu- larizing Text Categorization with Clusters of Words.” In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2016, pp. 1827– 1837 …
A Chinese Event Relation Extraction Model Based on BERT
C Tian, Y Zhao, L Ren – … on Artificial Intelligence and Big Data …, 2019 – ieeexplore.ieee.org
… July, Uppsala, Sweden. 2010. [8] Nguyen TH , Grishman R. Relation Extraction: Perspective from Convolutional Neural Networks.[C]// Workshop on Vector Space Modeling for Natural Language Processing. 2015. [9] Santos …
Sentence centrality revisited for unsupervised summarization
H Zheng, M Lapata – arXiv preprint arXiv:1906.03508, 2019 – arxiv.org
… Firstly, to better capture sentential meaning and compute sentence similarity, we employ BERT (Devlin et al., 2018), a neural representation learn- ing model which has obtained state-of-the-art re- sults on various natural language processing tasks … 2.1 Undirected Text Graph …
Viana: Visual interactive annotation of argumentation
F Sperrle, R Sevastjanova, R Kehlbeck… – arXiv preprint arXiv …, 2019 – arxiv.org
… till the building blocks of argumentation from a text corpus, it is not sufficient to employ off-the-shelf Natural Language Processing techniques … Al- ternative designs would remove either the relation annotation (note taking view) or the text (graph views) to lower the information con …
Exploiting semantic web knowledge graphs in data mining
P Ristoski – 2019 – books.google.com
… Such knowledge graphs contain factual knowledge about real word entities and the relations be- tween them, which can be utilized in various natural language processing, infor- mation retrieval, and any data mining applications …
Fine-Grained Image Classification Combined with Label Description
X Shi, L Xu, P Wang – … on Tools with Artificial Intelligence (ICTAI …, 2019 – ieeexplore.ieee.org
… image information, natural language description features can complement the detailed features of the image … We treat the documents (label descriptions) and all the words in the label descriptions as nodes to construct a large heterogeneous text graph proposed in TextGCN [40 …
Distilling Semantic Topics From Documents
M Moilanen – 2019 – jultika.oulu.fi
… Keywords: natural language processing, entity linking, automatic keyphrase extraction, automatic topic labeling, graph theory, ontology, knowledge base, information retrieval Page 3. Moilanen M. (2019) Topic Distiller: Semanttisten aiheiden suodattaminen dokumenteista …
Extractive summarization using semigraph (ESSg)
S Sonawane, P Kulkarni, C Deshpande, B Athawale – Evolving Systems, 2019 – Springer
… In abstractive summarization the semantic representation and generation of natural language is complex as compared to sentence extraction … They take into account the global information and recursively calculate the sentence significance from the entire text graph, rather than …
Chinese News Keyword Extraction Algorithm Based on TextRank and Topic Model
A Xiong, Q Guo – International Conference on Artificial Intelligence for …, 2019 – Springer
… It defines the diffusivity of two candidate words, constructs a new weight formula, and improves the weight of the edges in the text graph … In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–441 …
A Comparative Study on different Keyword Extraction Algorithms
MG Thushara, T Mownika… – 2019 3rd International …, 2019 – ieeexplore.ieee.org
… TextRank does not depend on a text unit’s (vertex) native context, but depends upon the entire text (graph) from which the … for a given research paper from a substantial collected text, and have displayed their ability in enhancing numerous natural language processing (NLP …
Panorama: BBN Participation in SM-KBP 2019
S Adali, R Bock, D Ellard, J Fasching, J Greve, I Heintz… – tac.nist.gov
… event ontology [5]. ACCENT finds events using structured patterns applied to augmented text graphs (normalized proposition … and A. Zamanian, “SERIF Language Processing – Effective Trainable Language Understanding,” in Handbook of Natural Language Processing and …
Semi-automatic System for Title Construction
S Duari, V Bhatnagar – International Conference on Information …, 2019 – Springer
… Automatic generation of full-fledged title for scientific write-up is a complex process that requires natural language generation, which is still … This can be improved by including more part-of-speech categories to the text graph after extensively studying the distribution of title-words …
GKR: Bridging the gap between symbolic/structural and distributional meaning representations
AL Kalouli, R Crouch, V dePaiva – … of the First International Workshop on …, 2019 – aclweb.org
… left). However, the difference between con- junction and disjunction is mirrored in the con- text graph: there, disjunction introduces one ad- ditional context for each component of the com- plex concept (Figure 2, right). These …
Realizing the concept of “Multiple Representations” by using CAS
H Heugl – resources.t3europe.eu
… Mathematical concepts are presented in multiple modes of representation (or “proto- types”) such as text, graphs and diagrams, tables … classes – symbols and icons Descriptive representations include information given by words or sentences in the natural language or symbols …
Transformation-based processing of typed resources for multimedia sources in the IoT environment
H Gao, Y Duan, L Shao, X Sun – Wireless Networks, 2019 – Springer
… {\text{Graph}}_{\text{DIK}} \,{ left( {{\text{DataGraph}}_{\text{DIK}} } \right),\left( {{\text{ InformationGraph}}_{\text{DIK … Customers express their information need in natural language, and this complex requirement information could contain inconsistencies, redundancy and …
Identification of User Aware Rare Sequential Pattern in Document Stream-An Overview
RR Shelke – 2019 – academia.edu
… To select the best aliases among the extracted candidates, we propose numerous ranking scores based upon three approaches: lexical pattern frequency, word co-occurrences in an anchor text graph, and page … Conf. Computational Natural Language Learning (CoNLL ’03), pp …
Keyphrase Generation Based on Self-Attention Mechanism
K Yang, Y Wang, W Zhang, J Yao, Y Le – pdfs.semanticscholar.org
… The former methods include finding the central nodes of text graph [Grineva, Grinev and Lizorkin (2009)], topical clusters [Liu, Huang, Zheng … 3.2.2 Multi-head attention The attention mechanism has been widely applied to various tasks of natural language processing based on …
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
MJ Smith, C Sala, JM Kanter… – arXiv preprint arXiv …, 2019 – arxiv.org
… Finally, we create and describe a general-purpose, multi-task, end-to-end AutoML system that provides solutions to a variety of ML problem types (classification, regression, anomaly detection, graph matching, etc.) and data modalities (image, text, graph, tabular, relational, etc.) …
Systems for Concurrent Real-time Graph Analytics
P Kumar – 2019 – search.proquest.com
… the entities, relationships and their properties with the help of various tools. For example, natural language processing tools can pre-process the unstructured text data to identify the entities, relationship and their properties. The whole data then …
Computational analysis of financial narratives: Overview, critique, resources and future directions
M El-Haj, P Rayson, M Walker, S Young… – Journal of Business …, 2019 – ucd.ie
… Second, we discuss the applicability of a series of established methods from the natural language processing and corpus … Page 4. 2 natural language processing (NLP) and corpus linguistics literatures that have yet to gain traction …
Multimodal classification of urban micro-events
M Sukel, S Rudinac, M Worring – Proceedings of the 27th ACM …, 2019 – dl.acm.org
… Classifier Features F1 LR text, graph, prob_time, prob_image 0.882 XGB geo, image, graph, geo_hist, prob_text 0.875 LR text 0.865 XGB text 0.851 LR graph 0.844 XGB graph 0.810 XGB prob_image, prob_geo_hist, 0.737 prob_weather, time, geo LR image, geo_hist …
A Benchmark Dataset and Case Study for Chinese Medical Question Intent Classification
N Chen, X Su, T Liu, Q Hao, M Wei – imu-ai.com
… of knowledge representation, knowledge graph-based med- ical QA systems gradually attract more and more attention [2, 3]. They not only allow users to ask medical questions in natural language but also … TextGCN: TextGCN builds a large and heterogeneous text graph on the …
Multi-Task Learning for Abstractive and Extractive Summarization
Y Chen, Y Ma, X Mao, Q Li – Data Science and Engineering, 2019 – Springer
… document into short sentences. Multi-task learning method has been successfully applied in a wide range of tasks across computer vision [18], speech recognition [12] and natural language processing [11]. It improves generalization …
Artificial intelligence for the early design phases of space missions
A Berquand, F Murdaca, A Riccardi… – 2019 IEEE …, 2019 – ieeexplore.ieee.org
… Page 6. 6 DEA-SQUID-INT-07 smart-squid outputs shall be displayed in natural language in different format (ie, images, text, graph) via the User Interface. DEA-SQUID-INT-08 Traceability: smart-squid shall provide traceability and access to the source of information …
A Scalable and Distributed Actor-Based Version of the Node2Vec Algorithm
G Lombardo, A Poggi – ceur-ws.org
… key features of the data is common in different Data mining and Machine Learning tasks (eg Natural Language Processing, Computer … works) that requires an euclidean feature representation to work with non-euclidean heterogeneous data, such as text, graph-data, collection of …
Study of probabilistic topic representations for the classification of genomic elements
NP Gialitsis, P Stamatopoulos – 2019 – researchgate.net
… We treat DNA as a natural language, by assigning documents and words to genomic classes … SUBJECT AREA: Machine Learning KEYWORDS: genomics, topic-model, representation, bioinformatics, natural-language-processing Page 6.
Géneros textuales y dificultades de redacción en ámbitos especializados
I da Cunha, MA Montané – Revista signos, 2019 – scielo.conicyt.cl
… and Digital Agenda, 2015 ) indicates. The general objective of this plan is to encourage the development of natural language processing and automatic translation in Spanish and co-official languages. The main domains of …
Geoscience keyphrase extraction algorithm using enhanced word embedding
Q Qiu, Z Xie, L Wu, W Li – Expert Systems with Applications, 2019 – Elsevier
… & Bulut, 2016). Automatically extracting the proper keyphrases of a given document has become an important research direction, and it includes text mining, natural language processing and information retrieval. In recent years …
Learning Graphical Structure of Electronic Health Records with Transformer for Predictive Healthcare
E Choi, MW Dusenberry, G Flores, Z Xu, Y Li, Y Xue… – graphreason.github.io
… of the structural information of hospital en- counter records. Transformer (Vaswani et al., 2017) was proposed for natural language processing, specifically machine translation. It uses a novel method to process sequence data …
Predictive modelling of stigmatized behaviour in vaccination discussions on Facebook
N Straton, R Ng, H Jang, R Vatrapu… – … on Bioinformatics and …, 2019 – ieeexplore.ieee.org
… LSTM was used to learn text represen- tation in [18], [19]; LSTM and BiLSTM were used as a benchmark classification model in comparison to Text GCN (Text Graph Convolutional Networks) performance in [16] … Class-based n-gram models of natural language …
Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
A Kandwal, Z Nie, L Wang – mhealth.jmir.org
… human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The …
Exploitation of Visual Relationships for Semantic Image Understanding
??? – 2019 – s-space.snu.ac.kr
… The intention of this dissertation is to propose novel methods that can be used to construct a graphical structure of relations in diagrams and exploit se- mantic contexts with natural language to solve a multimodal problem. More …
The advances of stemming algorithms in text analysis from 2013 to 2018
YY Liu – 2019 – repository.up.ac.za
Page 1. FACULTY OF ENGINEERING, BUILT ENVIRONMENT AND INFORMATION TECHNOLOGY FAKULTEIT INGENIEURSWESE, BOU-OMGEWING EN INLIGTINGTEGNOLOGIE INDIVIDUAL ASSIGNMENT / INDIVIDUELE WERKSOPDRAG Surname / Van Liu …
Generating Descriptive and Accurate Image Captions with Neural Networks
L Wu – 2019 – opus.lib.uts.edu.au
… Page 7. ABSTRACT Image captioning is to automatically describe an image with a sentence, which is a topic connecting computer vision and natural language processing. Research on … Generally, the communication between humans is through the natural language. The …
Contron: Continuously trained ontology based on technical data sheets and wikidata
K Opasjumruskit, D Peters, S Schindler – arXiv preprint arXiv:1906.06752, 2019 – arxiv.org
… Natural Language Processing (NLP) can be used to help analyze the context of data sheets and choose the best-matched definition of the … Issues such as a multiple columns layout mixed with text, graphs, and tables can be solved using layout-aware text extraction techniques …
Comprehensive overview of computer-based health information tailoring: a systematic scoping review
AK Ghalibaf, E Nazari, M Gholian-Aval, M Tara – BMJ open, 2019 – bmjopen.bmj.com
… The analysis showed that only 13% of the studies described the tailoring algorithm they used, from which two approaches revealed: information retrieval (12%) and natural language generation (1%). The systematic mapping of the delivery channel indicated that nearly half of …
Unsupervised learning strategies for automatic generation of personalized summaries
V Woloszyn – 2019 – lume.ufrgs.br
… Figure 5.1 Similarity using Jaccard Index of the user’s review with a Summary, The Most Helpful Review, and All Reviews about the same product …..61 Figure 5.2 A simple text graph…..63 Figure 5.3 Interest dampening …
A survey of 3D indoor scene synthesis
SH Zhang, SK Zhang, Y Liang, P Hall – Journal of Computer Science and …, 2019 – Springer
… [64] is a special dataset. Its scenes are annotated in natural language. In addition to scene datasets, 3D models reposito- ries are also needed, eg, ShapeNet[65,66]. They have annotations for each 3D model, including the cate- gory, transformation, front orientation, etc …
Handling Big Data Scalability in Biological Domain Using Parallel and Distributed Processing: A Case of Three Biological Semantic Similarity Measures
AM Almasoud, HS Al-Khalifa… – BioMed research …, 2019 – hindawi.com
… Volume refers to the increasing size of data. Variety refers to the types of data including text, graphs, images, video, audio, and other types. Velocity means that data are generated continuously as a stream at high speeds and needs to be processed as they are generated …
Next Level: A Course Recommender System Based On Career Interests
S Shahab – 2019 – scholarworks.sjsu.edu
Page 1. San Jose State University SJSU ScholarWorks Master’s Projects Master’s Theses and Graduate Research Spring 5-20-2019 NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS Shehba Shahab San Jose State University …
Extracting Fine-grained Knowledge Units from Texts with Deep Learning
L Yu, L Qian, C Fu, H Zhao – Data Analysis and …, 2019 – manu44.magtech.com.cn
… by Extracting Key Aspects of Scientific Papers[C]// Proceedings of the 5th International Joint Conference on Natural Language Processing, 2011 … Study on Textual Topic Identification by Clustering Clique Structure in Multi-Relationship Text Graph[J]. Journal of the China Society …
Exploiting Textual Content in Academic Citation Networks
S Ganguly – 2019 – web2py.iiit.ac.in
… As our first contribution, we develop an unsupervised mechanism leveraging natural language processing techniques to solve this problem … We treat algorithm names as named entities and use natural language processing techniques to extract them …
Improving Performance of Convolutional Neural Networks via Feature Embedding
T Ghoshal, S Zhang, X Dang, D Wilkins… – Proceedings of the 2019 …, 2019 – dl.acm.org
… The datasets and experimental setups have been dis- cussed in Section 5. We present our results and findings in Section 6. In Section 7, we provide conclusion. 2 RELATED WORK Examples of non-image data include text, graphs, manifolds etc …
Scanning Single Shot Detector for Math in Document Images
PS Mali – 2019 – search.proquest.com
… variables. Many embedded math expres- sions also have their explicit natural language definitions like ‘where w is the set of words’. Iwatsuki et al … areas. In the first step, they extract text, graph and image stream from the PDF document …
An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval
L Zhu, J Long, C Zhang, W Yu, X Yuan, L Sun – IEEE Access, 2019 – ieeexplore.ieee.org
… [60] presented a multi-modal web image retrieval technique to leverage the heteroge- neous data on the web to improve retrieval precision. In this solution, three graphes, ie, Content-Graph, Text-Graph and Link-Graph which are constructed on visual content features, 180574 …
THE BULLETIN OF THE
S Plouffe, H Heugl, C Kennedy, M Beaudin, J Böhm – 2019 – austromath.at
Page 1. THE DERIVE – NEWSLETTER #115 ISSN 1990-7079 THE BULLETIN OF THE USER GROUP C ontents: 1 Letter of the Editor 2 Editorial – Preview 3 DERIVE & CAS-TI User Forum Once more DERIVE & WIN10, Alfred Roulier’s website …
Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation
MM Rahman – 2019 – harvest.usask.ca
… 28 3.3.2 Text Preprocessing . . . . . 28 3.3.3 Text Graph Development . . . . . 28 3.3.4 TextRank (TR) Calculation …
In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse
M El?Haj, P Rayson, M Walker… – Journal of Business …, 2019 – Wiley Online Library
… Finally, Fisher et al. (2016) synthesize the stream of natural language processing (NLP) research utilizing AF data and identify paths for future research … 3.3. Natural language processing Natural language processing (NLP) sits at the core of computational linguistics …
Authority and priority signals in Online Reputation Monitoring
J Rodriguez Vidal – 2019 – e-spacio.uned.es
… 40 2.3.2.1 Natural Language generation . . . . . 40 2.3.2.2 Integer Linear Programming . . . . . 41 … 44 2.3.3.1 Generating reports using templates . . . . . 45 2.3.3.2 Generating reports using Natural Language gen- eration …
Deep learning intervention for health care challenges: Some biomedical domain considerations
I Tobore, J Li, L Yuhang, Y Al-Handarish… – JMIR mHealth and …, 2019 – mhealth.jmir.org
… human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The …
Efficient Matrix-aware Relational Query Processing in Big Data Systems
Y Yu – 2019 – hammer.figshare.com
… The fast development of IoT and remote sensing gives birth to a diversity of data types, such as structured data (eg, relational tables), unstructured data (eg, text, graph, images, audios, and … Motivating Scenario 4: Natural Language Processing – Word2Vec …
Promotional Campaigns in the Era of Social Platforms
NE Abu-el-rub – 2019 – digitalrepository.unm.edu
Page 1. University of New Mexico UNM Digital Repository Computer Science ETDs Engineering ETDs Spring 7-1-2019 Promotional Campaigns in the Era of Social Platforms Noor E. Abu-el-rub University of New Mexico Follow …
Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings
Q Yang, ZH Zhou, Z Gong, ML Zhang, SJ Huang – 2019 – books.google.com
Page 1. Qiang Yang · Zhi-Hua Zhou · Zhiguo Gong · Min-Ling Zhang · Sheng-Jun Huang (Eds.) Advances in Knowledge Discovery and Data Mining 23rd Pacific-Asia Conference, PAKDD 2019 Macau, China, April 14–17, 2019 Proceedings, Part III 123 Page 2 …
Innovations in the use of data facilitating insurance as a resilience mechanism for coastal flood risk
AG Rumson, SH Hallett – Science of the Total Environment, 2019 – Elsevier
… IoT Internet of Things. LM TOM London Market Target Operating Model. NLP Natural Language Processing. QA Quality Assurance. RDBMS Relational Database Management System. SaaS Software as a Service. SAR Synthetic Aperture Radar. SQL Structured Query Language …
Big Data
A Maheshwari – 2019 – books.google.com
… CASELET IBM Watson: A Big Data System IBM created the Watson system as a way of pushing the boundaries of Artificial Intelligence and natural language understanding technologies … Data types range from numbers to text, graph, map, audio, video and others …
Artificial Neural Networks and Machine Learning-ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany …
IV Tetko, V K?rková, P Karpov, F Theis – 2019 – books.google.com
Page 1. Igor V. Tetko · Ve?ra Ku?rková· Pavel Karpov · Fabian Theis (Eds.) Artificial Neural Networks and Machine Learning–- ICANN 2019 Deep Learning 28th International Conference on Artificial Neural Networks Munich, Germany, September 17–19, 2019 Proceedings, Part II …
Mobile Location Based Indexing of Disaster Notification System
TT Zan – 2019 – onlineresource.ucsy.edu.mm
Page 1. MOBILE LOCATION BASED INDEXING OF DISASTER NOTIFICATION SYSTEM THU THU ZAN UNIVERSITY OF COMPUTER STUDIES, YANGON JANUARY, 2019 Page 2. Mobile Location Based Indexing of Disaster Notification System Thu Thu Zan …
Whole system railway modelling
GP Greenland – 2019 – etheses.bham.ac.uk
Page 1. School of Electronic, Electrical and System Engineering University of Birmingham Whole System Railway Modelling A thesis submitted to the University of Birmingham for the degree of DOCTOR OF PHILOSOPHY Author: Garry Patrick Greenland …