## Text Graphs & Natural Language

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:

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Keyword Extraction with Character-Level Convolutional Neural Tensor Networks
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Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation
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Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
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Extracting Keyphrases from Research Papers Using Word Embeddings
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… 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
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Novel Representations, Regularization & Distances for Text Classification
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A Chinese Event Relation Extraction Model Based on BERT
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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.

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 …