Name
Abstract | ||
|---|---|---|
We implement a scalable mechanism to build a taxonomy of entities which improves relevance of search engine in a vertical domain. Taxonomy construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Taxonomy and syntactic generalization is applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22688-5_8 | ICCS |
Keywords | Field | DocType |
evaluation result,taxonomy construction,relevance improvement,various search result,syntactic generalization,syntactic parse tree,new entity,taxonomy capture,scalable mechanism,search engine,commercial setting | World Wide Web,Search engine,Computer science,Natural language processing,Artificial intelligence,Parsing,Syntax,Scalability | Conference |
Volume | ISSN | Citations |
6828.0 | 0302-9743 | 7 |
PageRank | References | Authors |
0.42 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Galitsky | 1 | 248 | 37.81 |
| GáBor Dobrocsi | 2 | 32 | 2.01 |
| Josep Lluis de la Rosa | 3 | 95 | 14.92 |
| Sergei O. Kuznetsov | 4 | 1630 | 121.46 |