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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (12): 13-22    DOI: 10.11925/infotech.2096-3467.2022.0087
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Identifying Topic-Problem Instances Based on Syntactic Dependency Enhancement
Wang Lu1,2,Le Xiaoqiu1,2()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper aims to identify the defects, deficiencies, and difficulties of existing research on a given topic. [Methods] First, we transformed the topic-problem instance pair extraction to candidate phrase classification. Then, we extracted candidate phrases from the problem sentences, and constructed a syntactic dependency tree. Third, we built a syntactic dependency enhanced classification model based on BiGCN and Transformer interaction module, Fourth, we used this new model to identify the problem instances from the candidate phrases corresponding to a given topic. [Results] The proposed model effectively identified the problem instances and topic-problem instances. Its F1 value reached 83.7%, which is 2.8 percentage point higher than the baseline model. [Limitations] We did not examine the referential relationship between sentences, which may omit some problem instances and reduce the recall rates. [Conclusions] The proposed model could effectively identify the topic and problem instances.
Key words： Problem Extraction      Syntactic Dependency      Transformer      Graph Neural Network     
Received: 29 January 2022      Published: 03 February 2023
ZTFLH:  TP391  
Corresponding Authors: Le Xiaoqiu，ORCID：0000-0002-7114-5544     E-mail: lexq@mail.las.ac.cn
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Lu Wang
Xiaoqiu Le

Cite this article:

Wang Lu, Le Xiaoqiu. Identifying Topic-Problem Instances Based on Syntactic Dependency Enhancement. Data Analysis and Knowledge Discovery, 2022, 6(12): 13-22.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0087     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I12/13

Processing Flow
主题 候选短语 标签
low-fidelity
model		 not very accurate 1
may be quick to evaluate 0
whereas a high-fidelity model may be computationally expensive to evaluate 0
provides an accurate estimate of the true performance 0
high-fidelity
model		 not very accurate 0
a low-fidelity model 0
may be quick to evaluate 0
may be computationally expensive to evaluate 1
provides an accurate estimate of the true performance 0
Examples of Candidate Phrase Extraction and Labeling
Schematic Diagram of Multi-Word Topic Merging
The Model Framework
Interaction Module between Flat Representation and Dependency Representation
模型 准确率/% 召回率/% F1值/% 句子级准确率/%
BiLSTM 82.3 79.9 80.9 75.9
本文 84.6 82.8 83.7 80.9
Results of Comparative Experiments
模型 准确率
/%		 召回率
/%		 F1值
/%		 句子级
准确率/%
Transformer 82.7 81.2 81.9 77.9
BiGCN 83.3 81.3 82.3 78.4
Transformer + BiGCN 83.4 80.9 82.1 78.2
本文 84.6 82.8 83.7 80.9
Results of Ablation Experiments
问题实例 模型 识别结果
1. Frequent changes in the relationship of members towards a community make the task of community detection even more challenging. Transformer /
BiGCN {<community detection； frequent changes in the relationship of members towards a community >}
Transformer + BiGCN {<community detection； frequent changes in the relationship of members towards a community >}
本文 {<community detection； frequent changes in the relationship of members towards a community >}
2. Most of the existing community detection approaches ignore node attributes information， which leads to poor results. Transformer {<community detection； ignore node attributes information>}
BiGCN {<community detection； ignore node attributes information， leads to poor results>}
Transformer + BiGCN {<community detection； ignore node attributes information >}
本文 {<community detection； ignore node attributes information， leads to poor results>}
3. The most basic and significant issue in complex network analysis is community detection， which is a branch of machine learning. Transformer /
BiGCN {<community detection； is a branch of machine learning >}
Transformer + BiGCN {<community detection； is a branch of machine learning >}
本文 /
Example of Experimental Results
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