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Title: Syntactic based approach for grammar question retrieval
Authors: Fang, Lanting
Tuan, Luu Anh
Hui, Siu Cheung
Wu, Lenan
Keywords: Grammar Question Retrieval
DRNTU::Engineering::Computer science and engineering
Syntactic Tree
Issue Date: 2018
Source: Fang, L., Tuan, L. A., Hui, S. C., & Wu, L. (2018). Syntactic based approach for grammar question retrieval. Information Processing and Management, 54(2), 184-202. doi:10.1016/j.ipm.2017.11.004
Series/Report no.: Information Processing and Management
Abstract: With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions’ grammatical focus. Next, we propose two kernel functions, namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy. The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis.
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2017.11.004
Rights: © 2017 Elsevier Ltd. All rights reserved. This paper was published in Information Processing and Management and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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