Dec 4, 2013 · In our solution, we first normalize the query and then compute the similarity between the query and each question using weighted edit distance.

ABSTRACT. In this paper, we describe our contribution to the FIRE. 2013 shared task on “FAQ Retrieval using Noisy Queries”.

Abstract: In this paper, we describe our contribution to the FIRE 2013 shared task on "FAQ Retrieval using Noisy Queries". Short messaging service (SMS) and ...

We first normalize the query, then compute the similarity between the query and each question using weighted edit distance. We also make use of a nearest ...

May 30, 2015 · In our proposed model, we use token based noise correction techniques i.e. LCS, Edit Distance. These techniques work based on the intuition that ...

Missing: Weighted Edit Distance

Weighted Edit Distance based FAQ Retrieval using Noisy Queries. FIRE 2013: 8:1-8:4. [+][–]. Coauthor network. maximize. Note that this feature is a work in ...

Weighted edit distance based faq retrieval using noisy queries. S Mhaisale, S Patil, K Mahamuni. Proceedings of the 4th and 5th Annual Meetings of the Forum ...

▫ First idea: retrieve dictionary terms close (in weighted edit distance) to each query term. ▫ Now try all possible resulting phrases with one word. “fixed ...

Weighted edit distance based faq retrieval using noisy queries. In FIRE. [Palangi et al. 2016] Palangi, H.; Deng, L.; Shen, Y.; Gao, J.;. He, X.; Chen, J ...