TY - GEN
T1 - Question-formed Query Suggestion
AU - He, Yuxin
AU - Mao, Xianling
AU - Wei, Wei
AU - Huang, Heyan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Traditional Query Suggestion (TQS) aims to retrieve or generate completed queries given input keywords and query logs, which plays a vital role in information retrieval. Nearly all existing TQS methods obtain suggested queries, which are usually in the form of keywords or phrases. However, queries like keywords or phrases suffer from incomplete or ambiguous se-mantics. Ideally, question-formed queries are more intuitive and closer to the information needs of users, which can improve their satisfaction during a search. Motivated by this idea, thus, this paper defines a novel question-formed query suggestion task that generates question-formed queries given input keywords and web page texts. Moreover, we also propose a novel pipeline method for this novel task. Specifically, a query generation module is first employed to generate related question-formed queries given keywords and web page texts. Then, a selection module selects the most representative tops among all generated queries as the final suggestion. Extensive experiments demonstrate that our method outperforms the state-of-the-art baselines in human evaluation.
AB - Traditional Query Suggestion (TQS) aims to retrieve or generate completed queries given input keywords and query logs, which plays a vital role in information retrieval. Nearly all existing TQS methods obtain suggested queries, which are usually in the form of keywords or phrases. However, queries like keywords or phrases suffer from incomplete or ambiguous se-mantics. Ideally, question-formed queries are more intuitive and closer to the information needs of users, which can improve their satisfaction during a search. Motivated by this idea, thus, this paper defines a novel question-formed query suggestion task that generates question-formed queries given input keywords and web page texts. Moreover, we also propose a novel pipeline method for this novel task. Specifically, a query generation module is first employed to generate related question-formed queries given keywords and web page texts. Then, a selection module selects the most representative tops among all generated queries as the final suggestion. Extensive experiments demonstrate that our method outperforms the state-of-the-art baselines in human evaluation.
KW - Information retrieval
KW - K-means
KW - Query suggestion
KW - Question generation
KW - Question-formed query suggestion
UR - http://www.scopus.com/inward/record.url?scp=85125067048&partnerID=8YFLogxK
U2 - 10.1109/ICKG52313.2021.00071
DO - 10.1109/ICKG52313.2021.00071
M3 - Conference contribution
AN - SCOPUS:85125067048
T3 - Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021
SP - 482
EP - 489
BT - Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021
A2 - Gong, Zhiguo
A2 - Li, Xue
A2 - Oguducu, Sule Gunduz
A2 - Chen, Lei
A2 - Manjon, Baltasar Fernandez
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Big Knowledge, ICBK 2021
Y2 - 7 December 2021 through 8 December 2021
ER -