Name
Abstract | ||
|---|---|---|
Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of c-QA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline. |
Year | Venue | DocType |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | Citations | PageRank |
abs/1803.03178 | 3 | 0.39 |
References | Authors | |
16 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsvetomila Mihaylova | 1 | 5 | 1.10 |
| Preslav I. Nakov | 2 | 1771 | 138.66 |
| Màrquez, Lluís | 3 | 2149 | 169.81 |
| Alberto Barrón-Cedeño | 4 | 346 | 29.35 |
| Mitra Mohtarami | 5 | 6 | 0.75 |
| Georgi Karadjov | 6 | 32 | 5.89 |
| James Glass | 7 | 3123 | 413.63 |