# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 1 Introduction  
The expansion of the internet and new learning media, coupled with an information explosion, has brought seemingly limitless knowledge enrichment [1]. The era of big data has rendered it necessary to extract and organize domain knowledge from a vast amount of information; doing so lessens people's likelihood of becoming disoriented when working online. Experts initially extracted conceptual knowledge manually from domain texts. This process promoted information comprehension and dissemination [2]. However, manual extraction requires substantial time and effort, and different experts may possess varied understandings of the same concept. The growth of the internet has meant conventional concept extraction no longer meets the demand for handling large volumes of online data or updating domain knowledge in a timely fashion. Considerable research has therefore aimed to develop more streamlined, sophisticated techniques for automated concept extraction [3, 4]. The generic paradigms of supervised and unsupervised approaches for automated concept extraction are valuable [2; 4; 5]. A sufficient number of pre-annotated training samples are necessary for supervised extraction. This method converts the concept extraction problem into a classification problem, where a classification model is established to categorize numerous concepts. Yet this technique calls for annotating data by hand, with the quality of annotation directly affecting models' extraction performance. Another paradigm, unsupervised concept extraction, consists of rule-based methods [6; 7], dictionary-based methods [8], statistical methods [9; 10], and semantic-based methods [11; 12]. Each category has benefits and drawbacks. For instance, dictionary-based methods are known for their speed and effectiveness, owing to well-constructed dictionaries. However, they tend to disregard important concepts and may produce erroneous outcomes. Statistical methods are comparatively efficient, cost-effective, and objectively reliable. Nonetheless, these approaches cannot accurately extract all target domain concepts due to inherent fuzziness and ambiguity in their intrinsic meaning and boundaries; such problems are even more pronounced for noisy datasets. To mitigate these issues in domain concept extraction, semantic information has been used to normalize concepts (i.e., by aligning extracted concepts with a domain-specific context). This strategy enhances the accuracy, coverage, and interoperability of domain concept extraction [11; 12; 13]. Briefly, automated concept extraction is a multifaceted and arduous task in which several techniques are combined to refine task performance. Few studies have sought to improve concept extraction methods' explainability and robustness. Further exploration is thus needed to improve learning and instruction in educational settings (e.g., online learning and intelligent education). In the education field, foundational subject-related knowledge reflects concepts frequently expressed as single words or phrases. Educational concepts must be blended into learning and instructional practices; students' inadequate conceptual understanding can cause them to forget information during the learning process [14; 15]. Similarly, instructors can enhance learning materials' quality through a clear sense of subject-specific concepts [16; 17]. Extracting concepts from various subject areas based on extensive unstructured text is thus critical for instructors and students, especially to enrich teaching and learning activities. Yet educational concept extraction faces certain challenges. Firstly, the educational domain consists of a range of topics and bodies of knowledge, each featuring distinct concepts and terminology. Accurately extracting educational concepts requires an in-depth understanding of the subject matter as well as contextual awareness. Secondly, these concepts are often polysemous and nuanced, such that one concept can have multiple meanings and usages.