Is Entity Extraction Different from Keyword Extraction

What Keyword Extraction Produces

Keyword extraction algorithms (TF-IDF, RAKE, TextRank, YAKE) analyze a document and return the most statistically important words or phrases. A document about deploying a Redis cluster might produce keywords like: "Redis," "cluster," "deployment," "replication," "configuration," "sentinel," "failover." These keywords tell you what the document is about, in terms of topic. They are useful for search indexing, document classification, and topic modeling.

Keywords have no type, no identity, and no relationships. "Redis" is just a statistically prominent string. It might be the first word in a sentence, a brand name, a person's name, or a configuration value. The keyword extractor does not know or care. It measures statistical importance, not semantic meaning.

What Entity Extraction Produces

Entity extraction identifies the specific things mentioned in text and classifies them. From the same Redis deployment document, entity extraction produces: Redis (Infrastructure), Redis Sentinel (Infrastructure), Redis Cluster (Technology), the platform team (Organization), AWS ElastiCache (Service). Each entity has a type, a canonical name, and potentially relationships to other entities: Redis Cluster uses Redis Sentinel for failover, the platform team maintains Redis Cluster, Redis Cluster deployed on AWS ElastiCache.

This structured output is qualitatively different from keywords. Entities can be stored as nodes in a graph. Relationships can be stored as edges. The graph can be traversed to answer questions that keyword search cannot: "what does the platform team maintain," "what depends on Redis," "what is deployed on AWS ElastiCache."

Where They Overlap

Many entity names are also keywords. "Redis" is both a prominent keyword and an Infrastructure entity. "PostgreSQL," "Kubernetes," and "authentication" are likely to appear in both keyword and entity extraction results. The overlap is strongest for nouns, especially proper nouns, which tend to be both statistically prominent and entity mentions.

The overlap creates a common misconception that entity extraction is "just keyword extraction with types." It is not. Entity extraction also identifies entities that are not statistically prominent (a service name mentioned once in passing is still an entity), excludes prominent words that are not entities ("deployment," "configuration," "best practices" are important keywords but not entities), and extracts relationships that keywords cannot represent.

When to Use Each

Use keyword extraction when you need to index documents for text search, classify documents by topic, generate document summaries or tag clouds, or build a search engine where users type natural language queries against a document corpus. Keywords are cheap to extract (no LLM needed), fast, and directly useful for search ranking.

Use entity extraction when you need to build a knowledge graph, enable relationship-based queries, connect information across documents through shared entities, or support multi-hop reasoning. Entities are more expensive to extract but enable capabilities that keywords cannot provide.

Use both when your retrieval system combines text search with graph traversal. Keywords power the vector search component (through embedding or BM25 indexing), while entities power the graph traversal component. This is the approach used by GraphRAG systems and by Adaptive Recall's hybrid retrieval.

Adaptive Recall extracts entities, not keywords, because its value comes from the knowledge graph and cognitive scoring that entities enable. Spreading activation traverses entity connections to find related memories. Cognitive scoring weights memories by entity connectivity. These capabilities require structured entities with types and relationships, not statistical keyword lists.