Name
Title | ||
|---|---|---|
A Flexible, Efficient And Accurate Framework For Community Question Answering Pipelines |
Abstract | ||
|---|---|---|
Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing. |
Year | Venue | Field |
|---|---|---|
2018 | 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2018): PROCEEDINGS OF SYSTEM DEMONSTRATIONS | Data science,Pipeline transport,Question answering,Computer science,Natural language processing,Artificial intelligence |
DocType | Volume | Citations |
Conference | P18-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Romeo | 1 | 27 | 2.79 |
| Giovanni Da San Martino | 2 | 236 | 27.08 |
| Alberto Barrón-Cedeño | 3 | 346 | 29.35 |
| Alessandro Moschitti | 4 | 3262 | 177.68 |