Which indexing approach yields higher precision but lower recall due to standardized terms?

Which indexing approach yields higher precision but lower recall due to standardized terms?

• A. Natural Language
• B. Controlled Vocabulary
• C. Database Software
• D. Education Resources Information Center (ERIC)

Answer: (B)

In information retrieval, precision and recall describe how well an indexing approach returns relevant results. A controlled vocabulary uses a fixed, standardized set of terms to tag each document. Because every item is described with the same descriptors for the same concepts, a search using those terms tends to retrieve mostly relevant items, giving high precision. However, that fixed term set may not cover all synonyms, variations, or new ways people describe a topic, so some relevant documents aren’t matched and are missed—lower recall. In contrast, natural language indexing attaches many words from the text, capturing more possible expressions and thus increasing recall, but it can also bring in more irrelevant results, reducing precision. So using standardized terms yields higher precision but lower recall due to the closed vocabulary.

In information retrieval, precision and recall describe how well an indexing approach returns relevant results. A controlled vocabulary uses a fixed, standardized set of terms to tag each document. Because every item is described with the same descriptors for the same concepts, a search using those terms tends to retrieve mostly relevant items, giving high precision. However, that fixed term set may not cover all synonyms, variations, or new ways people describe a topic, so some relevant documents aren’t matched and are missed—lower recall. In contrast, natural language indexing attaches many words from the text, capturing more possible expressions and thus increasing recall, but it can also bring in more irrelevant results, reducing precision. So using standardized terms yields higher precision but lower recall due to the closed vocabulary.