science

Text graph

Text graph is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Text graph rather than just read about it. In short: In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation term disambiguation (topic-based) text summarization, relation extraction and textual entailment.

Key takeaways

Text graph belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Text graph to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Text graph from memory before moving on to harder problems.

Reference excerpt

In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation term disambiguation (topic-based) text summarization, relation extraction and textual entailment.

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.

TextGraphs Workshop series The TextGraphs Workshop series is a series of regular academic workshops intended to encourage the synergy between the fields of natural language processing (NLP) and 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 and semantic role labelling, got progressively larger with ontology learning and information extraction from large text collections. The 11th edition of the workshop (TextGraphs-11) will be collocated with the Annual Meeting of Association for Computational Linguistics (ACL 2017) in Vancouver, BC, 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 Document classification Document-term matrix Hyperlinking Graph database Wiki

References

External links Gabor Melli's page on text graphs Description of text graphs from a semantic processing perspective.

Worked examples

Example 1 — a first encounter with Text graph

Start with the simplest possible case. Write down what Text graph claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Text graph before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Text graph ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Text graph

In research: Text graph appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Text graph in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Text graph is common in secondary-school and first-year university syllabi. It links to neighbouring topics Natural language processing, so understanding it makes those chapters shorter.
In everyday life: Look for Text graph outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Text graph in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Text graph means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Text graph out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Text graph in simple terms?

Why does Text graph matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Text graph?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Text graph.