# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 5 Discussion  
As demonstrated through the experimental results, WERECE exhibits a strong ability in improving the effectiveness of educational concept extraction. A key feature of WERECE lies in that it imposes word re-embedding on a pretrained model to ensure the semantic information of educational concepts are represented precisely. This is consistent with the findings of [PERSON] et al. [19], who used pre-trained word embeddings derived from Wikipedia to outperform TF-IDF on educational concept extraction. Also, a manifold learning algorithm adopted in WERECE allows reuse of ready-made pretrained models, with the aim of saving time on training and improving the performance of educational concept extraction. This case highlights the importance of manifold learning in discovering the potential geometric relationships among concept representations in a high-dimensional semantic space. According to [PERSON] et al. [65], manifold learning-based re-embedding ensures the consistency of the geometric relationship among concept representations and the real semantic information among concepts, thereby improving the performance of downstream NLP tasks. Furthermore, the experimental results demonstrated that the Box-Cox transformation consistently boosts the effectiveness of WERECE's discriminant function. The Box-Cox transformation was conducive in mitigating the uncertainty factor of K-means clusters caused by the random selection of original data points. Such an aspect ties in well with previous studies showing the impact of the Box-Cox transformation on machine learning algorithms [66; 67]. WERECE achieved the best performance on educational concept extraction in the balance of precision and recall when compared to baselines. In light of our experimental findings, it is evident that both TF-IDF and TextRank present a deficiency in precision. This is attributed to their inadequate extraction of certain frequent candidate concepts as educational concepts while simultaneously disregarding infrequent educational concepts. This also accords with the findings of a great deal of the previous work in educational concept extraction [17; 19; 29; 61; 68]. For example, TF-IDF achieved an average precision of approximately 0.35 in educational concept extraction from MOOC (i.e., Massive Open Online Course) video lectures [19]; the average precision of educational concept extraction through TextRank is around 0.45 [29]. It was suggested that isolation forest shows the best effectiveness results in domain concept extraction [59]. However, based on the empirical evidence obtained from our experiments, this does not appear to be the case. Although isolation forest achieved the highest recall in education concept extraction, its precision and F1 score are unsatisfactory. A possible explanation for this inconsistency might be that isolation forest is sensitive to sample distribution in experimental datasets [64]. With regard to K-means and one-class SVM in our experiments, their performance broadly supports the work of other studies in education, information retrieval, and medical services. In E-learning systems, K-means can be employed to obtain domain concept extraction from scholarly articles; it achieved an average precision of 0.80 [69]. To construct a target-specific sentiment lexicon, one-class SVM yielded a decent outcome, demonstrating an average precision of 0.79 and an average recall of 0.75 [58]. In the field of medicine, K-means was  
Figure 7: Improvement of WERECE over one-class SVM in terms of evaluation metrics. used to produce semantic clusters to identify medical terms, which facilitates verification of the impact of medical terms used by doctors on the service quality of E-health [60]. Compared to baselines, the encouraging precision and recall of WERECE measured in our experiments strengthen the case for bringing semantic association and computation into domain concept extraction.