Must a text graph be drawn visually?

No. It can be stored and analyzed as a data structure without a diagram.

Can a node represent a whole sentence?

Yes. The appropriate unit depends on the task; words, sentences, entities and documents are all possible choices.

Does an edge need a weight?

No. Some graphs record only whether a relationship exists. Others assign weights such as co-occurrence counts or similarity scores, with clearly stated interpretations.

AI Glossary / Natural language and speech / Intermediate

Text graph

Definition

A text graph is a graph representation built from language material. Its nodes stand for units such as words, sentences, documents or entities, and its edges represent a defined relationship between those units. The graph's meaning depends on how those nodes and edges are constructed.

What it means

The word graph here means a network of nodes and links, not a bar chart of word counts. Turning text into a network makes some relationships easier to analyze, but it also selects which aspects of the original language are retained.

A word co-occurrence graph can connect words that appear near one another. A sentence graph can link sentences according to a similarity measure. An entity graph might instead use relationships extracted from statements. These are different representations and should not be interpreted in the same way.

An edge therefore needs a clear definition. “These words appeared together” is much weaker than “this company owns that company.” A visual line does not create factual support that was absent from the construction process.

How it works

A project chooses a node unit and a rule for adding edges. It specifies whether links are directed, whether repeated observations increase an edge's weight and which text boundaries constrain connections. Preprocessing can merge case variants, remove selected words or preserve distinct mentions.

After construction, algorithms can rank nodes, identify clusters or retrieve connected material. TextRank is a historical example of ranking text units on graphs for keyword extraction and sentence selection. Other tasks use different graph methods.

The graph must remain traceable to its source. If an edge comes from co-occurrence, record the relevant sentence or window. If it comes from a predicted relation, preserve that relation's evidence and uncertainty.

Evaluation should test the downstream result and the representation choices. A graph that looks visually persuasive may still be dominated by frequent boilerplate or incorrectly merged terms.

Three practical examples

A keyword-extraction prototype ranks words in a co-occurrence network to suggest terms for a document index.
A summarization study connects similar sentences and selects representative ones for human review.
An archive browser uses document links to help readers explore related material while keeping the relationship definition visible.

Think of it this way

Think of a map whose lines might mean roads, shared ownership or radio contact. The same arrangement of dots has different implications under each legend.

Likewise, a text graph needs an edge legend. Graph algorithms can analyze the chosen network, but they cannot repair a misleading explanation of what its links mean.

Worked example

Building a tiny co-occurrence graph

Take the invented three-token text “solar battery storage.” Make one node per distinct word, use undirected edges and connect only adjacent tokens. The graph has three nodes and two edges: solar - battery and battery - storage.

The node degrees are 1, 2 and 1 respectively. If degree is normalized by the two possible neighbors, the scores are 0.5 for solar, 1 for battery and 0.5 for storage. Battery is most connected under this particular rule.

Now change the rule to connect every pair within a three-token window. The solar - storage edge is added, producing a triangle with three edges. Every node now has degree 2 and normalized degree 1.

No text changed, yet the ranking by degree changed from a unique leader to a tie. This is a simple degree calculation, not a TextRank computation. It demonstrates why window size and edge construction belong in any description of the result. Neither graph proves a real-world relationship among solar generation, batteries and storage equipment.

Illustrative case study

A fictional document-management company adds a network view of related manuals. Users initially assume that a link means one manual explicitly references another. In fact, the prototype connects manuals sharing enough vocabulary.

The team changes the legend to say “text similarity,” provides the shared passages and separates actual citations into a different edge type. It also tests whether repeated warranty text is driving irrelevant connections.

The resulting graph is easier to interpret because its claims match its construction. The company does not claim that the network is a verified knowledge base or that central manuals are necessarily the most authoritative.

Common misconceptions and mistakes

Assuming all text graphs are knowledge graphs. Many encode statistical or structural associations rather than factual assertions.
Treating co-occurrence as causation. Appearing together establishes neither direction nor cause.
Calling a highly connected node important without defining the task. Common or generic words can be highly connected.
Merging every identical string into one entity. The same name can refer to different people or things.
Hiding preprocessing choices. Removing words or changing boundaries can materially alter the network.

Business use

Text graphs can support exploratory analysis, retrieval, keyword suggestion and summarization. They are especially useful when the user needs to inspect relationships among multiple text units rather than consume one opaque score.

Before adopting a graph view, state what action a connection should help someone take. A similarity link can guide further reading; an extracted ownership claim requires different evidence and review. Mixing them in one unlabeled network invites mistakes.

Track source coverage and graph changes over time. Adding many nearly identical documents can shift centrality even when the underlying subject has not changed. For large collections, measure construction and update costs as well as the usefulness of the displayed relationships.

Check your understanding

A network links two organizations because their names occur in the same paragraph. Can the interface label the link “business partner” without additional analysis?

Answer: No. The evidence establishes co-occurrence under the stated rule. A partnership label would add an unsupported claim.

Related terms

Graph
co-occurrence
TextRank
centrality
knowledge graph
entity resolution
extractive summarisation

Learn next

Read co-occurrence and centrality. Build two small graphs from the same sentence using different window sizes, and explain which conclusions change because the construction rule changed.

Sources

Mihalcea and Tarau, TextRank: Bringing Order into Texts (2004), especially Text as a Graph: https://aclanthology.org/W04-3252.pdf. Primary research illustrates application-dependent node and edge choices and graph-based ranking. The three-word graph and document-management case are original examples; their degree calculations are not claimed TextRank outputs.

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