Application of Knowledge Map Based on BiLSTM-CRF Algorithm Model in Ideological and Political Education Question Answering System

Abstract

Ideological and political education plays an important role in university education and is an important way to realize the function of educating people. It is of great significance to establish a perfect automatic question answering system for ideological and political education. Traditional automatic question answering methods usually rely on predicates and other prior information to achieve knowledge base question answering, which requires a lot of manpower and poor generalization ability. In order to solve this problem, this paper designs a question answering system for ideological and political education based on BiLSTM-CRF algorithm model (BiLSTM: Bidirectional Long Short-Term Memory and CRF: Conditional Random Fields). For the knowledge base question answering method with weak-dependent information, this paper combines BERT (Bidirectional Encoder Representation from Transformers) and BiLSTM-CRF network to extract the named entity in the questions and locate the triplet information related to the entity in the knowledge base. Through the answer matching network, the similarity score is marked for the answers in the triplet set, and the threshold selection strategy is used to select the answers that meet the requirements. And according to the similarity score from high to paper, it is presented to the user. The experimental results show that the method weakens the dependence on prior information, reduces manual intervention and ensures the quality of question answering, and completes the validity verification of the question answering system of ideological and political education.

Unmasking the challenges in ideological and political education in China: A thematic review

“…Some of the scholars studied around knowledge mapping and question answering in ideological and political education. Zhao and Liu（2022）introduced a question answering system that reduces reliance on prior information and enhances the quality of question answering [ 35 ]. The studies utilized AI techniques, including BiLSTM-CRF [ 35 ], BERT [ 34 ], and Long-Short-Term Memory Neural Network [ 31 ], to extract relevant information and provide accurate answers.…”

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

“…In recent years, KBQA technologies have begun to be applied in specific fields, e.g., biomedicine [ 35 ], finance [ 36 ], and education [ 37 ]. With the deepening of KBQA research in specific domains, some basic works have been carried out in bridge engineering related fields.…”

Construction of the Interactive Educational Knowledge Graph and Classification of Student Groups

“…Therefore, the combination of online and offline education, which complement each other, has become the mainstream educational approach nowadays [1][2][3][4][5][6][7][8][9][10]. In order to make students' learning plans more scientific and their study time more flexible, more and more online learning platforms are mining professional knowledge systems, building interactive educational knowledge graphs, integrating fragmented knowledge modules, and recommending them to appropriate students [11][12][13][14][15][16][17][18][19][20]. The attributes of the knowledge nodes in the interactive educational knowledge graph need to cater to students' online learning preferences, and at the same time the knowledge points and learning resources need to be integrated and systematized.…”