Learning to Collectively Link Entities
| Autor: | Sunny Raj Rathod, Kanika Agarwal, Pararth Shah, Ashish Kulkarni, Ganesh Ramakrishnan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Markov random field
Computer science business.industry Binary number Map inference Collaborative learning 02 engineering and technology Machine learning computer.software_genre Annotation 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence business Subgradient method computer Natural language |
| Zdroj: | CODS |
| DOI: | 10.1145/2888451.2888454 |
| Popis: | Recently Kulkarni et al. [20] proposed an approach for collective disambiguation of entity mentions occurring in natural language text. Their model achieves disambiguation by efficiently computing exact MAP inference in a binary labeled Markov Random Field. Here, we build on their disambiguation model and propose an approach to jointly learn the node and edge parameters of such a model. We use a max margin framework, which is efficiently implemented using projected subgradient, for collective learning. We leverage this in an online and interactive annotation system which incrementally trains the model as data gets curated progressively. We demonstrate the usefulness of our system by manually completing annotations for a subset of the Wikipedia collection. We have made this data publicly available. Evaluation shows that learning helps and our system performs better than several other systems including that of Kulkarni et al. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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