# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 2 Related Work
### Generic Concept Extraction Methods  
Concept extraction, a core NLP task, refers to identifying and extracting predefined concepts or patterns from textual data. This task is difficult given the complex and dynamic nature of language. Scholars have investigated concept extraction in numerous areas, such as clinical medicine, information retrieval, and automation engineering. A recent review of the clinical literature on this topic indicated that most approaches to extracting domain concepts from clinical text fall into two categories: (1) rule-based methods and (2) machine learning methods, including deep learning approaches and hybrid approaches [4]. Likewise, [PERSON] et al. suggested that concept extraction strategies related to information retrieval can be classified as either machine learning methods, corpus-based methods, glossary-based methods, or heuristic-based methods [23]. Currently, concept extraction often involves either supervised or unsupervised strategies. These approaches normally follow a four-step procedure of preprocessing, generating a list of candidate concepts, identifying concepts from candidates, and evaluating those concepts. Our work is an example of unsupervised concept extraction, a task that can be further divided into four groups (summarized in Table 1): rule-based methods, dictionary-based methods, statistical methods, and semantic-based methods. First, rules and patterns in rule-based methods are predefined to extract concepts from text. Regular expressions or pattern-matching techniques are prevalent. Rule-based concept extraction adheres to grammatical rules, semantic rules, and related aspects to process a corpus and extract multi-character units that conform to predefined rules. These units are eventually labeled as concepts. [PERSON] employed a rule-based method that involved transforming detected names according to Polish grammar rules, utilizing a user-friendly approach for specifying transformation patterns through annotations to extract concepts from unstructured Polish texts [6]. [PERSON] et al. developed a rule-based approach that relies on a system of language resources to tackle the multi-word term problem in domain concept extraction [7]. One benefit of rule-based methods is their capacity to manage patterns and implement domain-specific knowledge. Nevertheless, these approaches are labor-intensive and time-consuming to develop. They also may not capture linguistic diversity. Second,dictionary-based concept extraction methods use pre-defined dictionaries of concept keywords to extract relevant information. Words or phrases in the target text are compared with dictionary entries via similarity metrics or string-matching algorithms [8; 24]. These techniques are faster than rule-based methods but heavily depend on the dictionary's quality and coverage; they may include noise or miss concepts that are absent from the dictionary. Third, statistical methods emphasize modeling and analyzing potential patterns among domain concepts in a target text. Statistical metrics such as term frequency-inverse document frequency (TF-IDF), co-occurrence, and neighbors are popular heuristics when ranking candidate concepts. The TF-IDF method is premised on the fact that domain-specific concepts exhibit much higher frequencies in some domains than in others, akin to the word frequency patterns provided by TF-IDF [9]. Candidate domain concepts can also be ranked statistically by depicting the extracted concepts as nodes on a graph and appraising their roles using network properties such as concept centrality and connectivity. Concepts that occupy more prominent positions within the graph receive higher scores, reflecting the representativeness of both the node and the concept [5]. Graph-based methods for concept extraction include the TextRank [10] approach and its variations, such as Ne-rank [25], TopicRank [26], and MultipartiteRank [27].