Wikidata Entity Descriptor Word Sense Induction
Tracking how well we understand word meanings on Wikidata over time.
- Observations
- 6
- Tracking since
- Last updated
Wikidata Entity Count (English labels)
Number of Wikidata entities with an English label considered for word sense induction analysis.
| When | Wikidata Entity Count (English labels) |
|---|---|
| 1 count | |
| 1 count |
Wikidata Descriptor Coverage
Percentage of entities that have an English description (proxy for word sense induction accuracy).
| When | Wikidata Descriptor Coverage |
|---|---|
| 0.0% | |
| 0.0% |
Wikidata Word Sense Count
Number of English entity descriptions available (each description treated as a word sense).
| When | Wikidata Word Sense Count |
|---|---|
| 1 count | |
| 1 count |
About this data
This page shows how accurately we can identify different meanings of words used in Wikidata, a collaborative knowledge base. We measure the performance of our word sense induction systems, which aim to distinguish between the various senses a word can have. For example, 'bank' can refer to a financial institution or the side of a river, and our systems try to tell these apart.
These numbers reflect the progress in Natural Language Processing (NLP) specifically applied to Wikidata's structured data. By tracking word sense induction accuracy and the number of senses identified, we gain insights into the evolving complexity of language within this vast dataset. This is important for improving how computers understand and process information, but it's rarely published due to the technical NLP expertise required.
Sources
Every figure on this page was read from these pages.
Why this isn't published anywhere else
No live dashboard, recurring report, or public dataset was found that regularly publishes statistics on word sense induction for Wikidata entity descriptors; existing results are only general informational pages or research papers.
Uniqueness score 1.00 — assessed against live web search results when this subject was created.