# Text graph > Mediated Wiki article. Canonical URL: https://mediated.wiki/source/Text_graph > Markdown URL: https://mediated.wiki/source/Text_graph.md > Source: https://en.wikipedia.org/wiki/Text_graph > Source revision: 1340675652 > License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/) Text-structure representation using graph models In [natural language processing](/source/Natural_language_processing) (NLP), a **text graph** is a [graph representation](/source/Graph_representation) of a [text item](/source/Text_item) (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as [text condensation](https://en.wikipedia.org/w/index.php?title=Text_condensation&action=edit&redlink=1)[1] [term disambiguation](/source/Term_disambiguation)[2] (topic-based) [text summarization](/source/Text_summarization),[3] [relation extraction](/source/Relation_extraction)[4] and [textual entailment](/source/Textual_entailment).[5] ## Representation The semantics of what a text graph's nodes and edges represent can vary widely. Nodes for example can simply connect to tokenized words, or to domain-specific terms, or to entities mentioned in the text. The edges, on the other hand, can be between these text-based tokens or they can also link to a [knowledge base](/source/Knowledge_base). ## TextGraphs Workshop series The TextGraphs Workshop series[6] is a series of regular [academic workshops](/source/Academic_workshop) intended to encourage the synergy between the fields of [natural language processing](/source/Natural_language_processing) (NLP) and [graph theory](/source/Graph_theory). The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications that focused on single documents for part-of-speech tagging, [word-sense disambiguation](/source/Word-sense_disambiguation) and semantic role labelling, got progressively larger with [ontology learning](/source/Ontology_learning) and [information extraction](/source/Information_extraction) from large text collections. The [11th edition of the workshop (TextGraphs-11)](https://sites.google.com/site/textgraphs2017/) will be collocated with the Annual Meeting of [Association for Computational Linguistics](/source/Association_for_Computational_Linguistics) ([ACL 2017](http://acl2017.org/)) in [Vancouver](/source/Vancouver), [BC](/source/British_Columbia), [Canada](/source/Canada). ## Areas of interest - Graph-based methods for providing reasoning and interpretation of deep learning methods - Graph-based methods for reasoning and interpreting deep processing by neural networks, - Explorations of the capabilities and limits of graph-based methods applied to neural networks in general - Investigation of which aspects of neural networks are not susceptible to graph-based methods. - Graph-based methods for Information Retrieval, Information Extraction, and Text Mining - Graph-based methods for word sense disambiguation, - Graph-based representations for ontology learning, - Graph-based strategies for semantic relations identification, - Encoding semantic distances in graphs, - Graph-based techniques for text summarization, simplification, and paraphrasing - Graph-based techniques for document navigation and visualization - Reranking with graphs - Applications of label propagation algorithms, etc. - New graph-based methods for NLP applications - Random walk methods in graphs - Spectral graph clustering - Semi-supervised graph-based methods - Methods and analyses for statistical networks - Small world graphs - Dynamic graph representations - Topological and pretopological analysis of graphs - Graph kernels, etc. - Graph-based methods for applications on social networks - Rumor proliferation - E-reputation - Multiple identity detection - Language dynamics studies - Surveillance systems, etc. - Graph-based methods for NLP and Semantic Web - Representation learning methods for knowledge graphs (i.e., knowledge graph embedding) - Using graphs-based methods to populate ontologies using textual data, - Inducing knowledge of ontologies into NLP applications using graphs, - Merging ontologies with graph-based methods using NLP techniques. ## See also - [Bag-of-words model](/source/Bag-of-words_model) - [Document classification](/source/Document_classification) - [Document-term matrix](/source/Document-term_matrix) - [Hyperlinking](/source/Hyperlinking) - [Graph database](/source/Graph_database) - [Wiki](/source/Wiki) ## References 1. **[^](#cite_ref-1)** Reimer, Ulrich; Hahn, Udo (1988). ["Text condensation as knowledge base abstraction."](https://www.cs.columbia.edu/~gmw/candidacy/ReimerHahn88.pdf) (PDF). *Fourth Conference on Artificial Intelligence Applications*. 1. **[^](#cite_ref-2)** Massé, A. Blondin; Chicoisne, Guillaume; Gargouri, Yassine; Harnad, Stevan; Picard, Olivier; Marcotte, Odile (2008). ["How Is Meaning Grounded in Dictionary Definitions?"](http://aclweb.org/anthology//W/W08/W08-2003.pdf) (PDF). *Proceedings of TextGraphs-3 Workshop*. 1. **[^](#cite_ref-melli&al2006_3-0)** Melli, Gabor; Shi, Zhongmin; Wang, Yang; Liu, Yudong; Sarkar, Anoop; Popowich, Fred (2006). ["Description of SQUASH, the SFU Question Answering Summary Handler for the DUC-2006 Summarization Task"](http://www-nlpir.nist.gov/projects/duc/pubs/2006papers/duc06squash.pdf) (PDF). *Proceeding of Document Understanding Conference (DUC 2006)*. 1. **[^](#cite_ref-melli2010b_4-0)** Melli, Gabor (2010). [*Supervised Ontology to Document Interlinking*](http://www.gabormelli.com/Publications/2010/2010_SupervisedOntologyToDocumentInterlinking/2010_SupervisedOntologyToDocumentInterlinking.pdf) (PDF) (Ph.D.). Simon Fraser University. 1. **[^](#cite_ref-5)** MacCartney, Bill; renager, Trond G; de Marneffe, Marie-Catherine; Cer, Daniel; D. Manning, Christopher (2006). ["Learning to recognize features of valid textual entailments"](http://nlp.stanford.edu/~wcmac/papers/rte-naacl06.pdf) (PDF). *Conference on Human Language Technology & Conference of the North American Chapter of the Association of Computational Linguistics*. 1. **[^](#cite_ref-6)** ["Textgraphs"](http://www.textgraphs.org/). Retrieved 6 March 2017. ## External links - [Gabor Melli's page on text graphs](http://www.gabormelli.com/RKB/Text_Graph) Description of text graphs from a semantic processing perspective. --- Adapted from the Wikipedia article [Text graph](https://en.wikipedia.org/wiki/Text_graph) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Text_graph?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.