Name
Abstract | ||
|---|---|---|
The content selection component of a natural language generation system decides which information should be communicated in its output. We use information from reports on the game of cricket. We first describe a simple factoid-to-text alignment algorithm then treat content selection as a collective classification problem and demonstrate that simple 'grouping' of statistics at various levels of granularity yields substantially improved results over a probabilistic baseline. We additionally show that holding back of specific types of input data, and linking database structures with commonality further increase performance. |
Year | Venue | Keywords |
|---|---|---|
2009 | ENLG | database structure,collective classification problem,simple factoid-to-text alignment algorithm,increase performance,improved result,natural language generation system,input data,granularity yield,content selection,machine learning,content selection component |
Field | DocType | Citations |
Collective classification,Natural language generation,Computer science,Natural language processing,Artificial intelligence,Granularity,Probabilistic logic,Machine learning | Conference | 10 |
PageRank | References | Authors |
0.68 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Colin Kelly | 1 | 22 | 1.70 |
| Ann Copestake | 2 | 862 | 95.10 |
| Nikiforos Karamanis | 3 | 274 | 19.23 |