Abstract
The usefulness of FrameNet is affected by its limited coverage and non-standard semantics. This paper presents some strategies based on Linguistic Linked Open Data to fully exploit and broaden its coverage. These strategies lead to the creation of a novel resource, Framester, which serves as a hub between FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, as well as other resources. We also present a Word Frame Disambiguation, an application performing frame detection from text using Framester as a base. The results are comparable in precision to the state-of-the-art machine learning tool, but with a much higher coverage.
| Original language | English |
|---|---|
| Pages (from-to) | 23-31 |
| Number of pages | 9 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1699 |
| Publication status | Published - 1 Jan 2016 |
| Externally published | Yes |
| Event | 4th International Workshop on Linked Data for Information Extraction, LD4IE 2016 - Kobe, Japan Duration: 18 Oct 2016 → … |
Keywords
- Frame detection
- Frame semantics
- FrameNet
- FrameNet coverage
- Framester
- Knowledge graphs