Sequence to Sequence Learning for Query Expansion
Information Retrieval
2018-12-27 v1 Computation and Language
Machine Learning
Abstract
Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.
Cite
@article{arxiv.1812.10119,
title = {Sequence to Sequence Learning for Query Expansion},
author = {Salah Zaiem and Fatiha Sadat},
journal= {arXiv preprint arXiv:1812.10119},
year = {2018}
}
Comments
8 pages, 2 figures, AAAI-19 Student Abstract and Poster Program