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        <title>Subtask Mining from Search Query Logs for How-Knowledge Acceleration</title>
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        <namePart type="given">Chung-Lun</namePart>
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        <namePart type="given">Hsin-Hsi</namePart>
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            <title>Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)</title>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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    <abstract>How-knowledge is indispensable in daily life, but has relatively less quantity and poorer quality than what-knowledge in publicly available knowledge bases. This paper first extracts task-subtask pairs from wikiHow, then mines linguistic patterns from search query logs, and finally applies the mined patterns to extract subtasks to complete given how-to tasks. To evaluate the proposed methodology, we group tasks and the corresponding recommended subtasks into pairs, and evaluate the results automatically and manually. The automatic evaluation shows the accuracy of 0.4494. We also classify the mined patterns based on prepositions and find that the prepositions like “on”, “to”, and “with” have the better performance. The results can be used to accelerate how-knowledge base construction.</abstract>
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%0 Conference Proceedings
%T Subtask Mining from Search Query Logs for How-Knowledge Acceleration
%A Kuo, Chung-Lun
%A Chen, Hsin-Hsi
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F kuo-chen-2016-subtask
%X How-knowledge is indispensable in daily life, but has relatively less quantity and poorer quality than what-knowledge in publicly available knowledge bases. This paper first extracts task-subtask pairs from wikiHow, then mines linguistic patterns from search query logs, and finally applies the mined patterns to extract subtasks to complete given how-to tasks. To evaluate the proposed methodology, we group tasks and the corresponding recommended subtasks into pairs, and evaluate the results automatically and manually. The automatic evaluation shows the accuracy of 0.4494. We also classify the mined patterns based on prepositions and find that the prepositions like “on”, “to”, and “with” have the better performance. The results can be used to accelerate how-knowledge base construction.
%U https://aclanthology.org/L16-1198
%P 1248-1252

Markdown (Informal)

[Subtask Mining from Search Query Logs for How-Knowledge Acceleration](https://aclanthology.org/L16-1198) (Kuo & Chen, LREC 2016)

• Subtask Mining from Search Query Logs for How-Knowledge Acceleration (Kuo & Chen, LREC 2016)

ACL

• Chung-Lun Kuo and Hsin-Hsi Chen. 2016. Subtask Mining from Search Query Logs for How-Knowledge Acceleration. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1248–1252, Portorož, Slovenia. European Language Resources Association (ELRA).