Abstract
We study the impact of different types of features for question ranking in community Question Answering: bag-of-words models (BoW), syntactic tree kernels (TKs) and rank features. It should be noted that structural kernels have never been applied to the question reranking task, i.e., question to question similarity, where they have to model paraphrase relations. Additionally, the informal text, typically present in forums, poses new challenges to the use of TKs. We compare our learning to rank (L2R) algorithms against a strong baseline given by the Google rank (GR). The results show that (i) our shallow structures used in TKs are robust enough to noisy data and (ii) improving GR requires effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm.
| Original language | English |
|---|---|
| Title of host publication | CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 1997-2000 |
| Number of pages | 4 |
| Volume | 24-28-October-2016 |
| ISBN (Electronic) | 9781450340731 |
| DOIs | |
| Publication status | Published - 24 Oct 2016 |
| Event | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016 |
Other
| Other | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 |
|---|---|
| Country | United States |
| City | Indianapolis |
| Period | 24/10/16 → 28/10/16 |
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Keywords
- Community question answering
- Learning to rank
- Syntactic structures
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)
Cite this
Learning to re-rank questions in community question answering using advanced features. / Martino, Giovanni; Barron, Alberto; Romeo, Salvatore; Uva, Antonio; Moschitti, Alessandro.
CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016 Association for Computing Machinery, 2016. p. 1997-2000.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning to re-rank questions in community question answering using advanced features
AU - Martino, Giovanni
AU - Barron, Alberto
AU - Romeo, Salvatore
AU - Uva, Antonio
AU - Moschitti, Alessandro
PY - 2016/10/24
Y1 - 2016/10/24
N2 - We study the impact of different types of features for question ranking in community Question Answering: bag-of-words models (BoW), syntactic tree kernels (TKs) and rank features. It should be noted that structural kernels have never been applied to the question reranking task, i.e., question to question similarity, where they have to model paraphrase relations. Additionally, the informal text, typically present in forums, poses new challenges to the use of TKs. We compare our learning to rank (L2R) algorithms against a strong baseline given by the Google rank (GR). The results show that (i) our shallow structures used in TKs are robust enough to noisy data and (ii) improving GR requires effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm.
AB - We study the impact of different types of features for question ranking in community Question Answering: bag-of-words models (BoW), syntactic tree kernels (TKs) and rank features. It should be noted that structural kernels have never been applied to the question reranking task, i.e., question to question similarity, where they have to model paraphrase relations. Additionally, the informal text, typically present in forums, poses new challenges to the use of TKs. We compare our learning to rank (L2R) algorithms against a strong baseline given by the Google rank (GR). The results show that (i) our shallow structures used in TKs are robust enough to noisy data and (ii) improving GR requires effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm.
KW - Community question answering
KW - Learning to rank
KW - Syntactic structures
UR - http://www.scopus.com/inward/record.url?scp=84996558218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996558218&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983893
DO - 10.1145/2983323.2983893
M3 - Conference contribution
VL - 24-28-October-2016
SP - 1997
EP - 2000
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
ER -