# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 3 Dataset Identification for Keyword Extraction
### Mohler Dataset
#### 3.2.3 Dataset Quality  
The quality of the Mohler Automated Student Assessment Grade (ASAG) dataset has several factors that should be considered when working with the data. These include:  
Spelling errors: The dataset contains several spelling errors, such as \"levls\" instead of \"levels\", \"parameters\" instead of \"parameters\", \"reference\" instead of \"reference\", \"adress\" instead of \"address\", \"refing\" instead of \"refining\", \"ponters\" instead of \"pointers\", and \"perenthesis\" instead of \"parenthesis\". These errors can make it challenging to accurately assess and analyze student answers for keyword extraction. Short answers: Some of the student answers in the dataset are too short to effectively assess for keyword extraction. This can make it difficult to identify relevant keywords or phrases that are unique to a particular question or topic. Other quality factors: There may be other quality factors that affect the dataset, such as inconsistent formatting or grammar errors. These factors can make it challenging to accurately evaluate student answers and extract meaningful keywords or phrases. To address these quality factors, it may be necessary to conduct pre-processing steps to clean the data before conducting keyword extraction. This can include techniques such as spell-checking, grammar-checking, and removing short answers. Additionally, it may be necessary to train machine learning models or use crowdsourcing techniques to improve the accuracy of keyword extraction from the dataset. The summary of the Mohler dataset is presented in Table 4.  
KeyBERT, YAKE, and RAKE for Keyword Extraction on the Mohler Automated Student Assessment Grade (ASAG) Dataset  
Table 5 shows the scores generated by YAKE, RAKE, and KeyBERT applied to the same reference answer \"to simulate the behavior of portions of the desired software product\". Table 5 displays the results of applying the YAKE, RAKE, and KeyBERT algorithms to extract keywords from a given text. The keywords are listed in the first column, and the second column indicates the number of words in each keyword (i.e., the gram). The third, fourth, and fifth columns show the scores assigned to each keyword by YAKE, RAKE, \begin{table}
\begin{tabular}{l l} \hline \hline
**Factor** & **Description** \\ \hline \multirow{2}{*}{Dataset structure} & Contains ID, questions, reference answers, and student answers in variable lengths. Scored by human evaluators up to a scale of 0–5, an average score is also available. \\ \hline \multirow{2}{*}{Dataset complexity} & Diversity in student answers, length is not consistent, the dataset is biased towards correct answers, and the scores of human evaluators are different on a number of answers. \\ \hline \multirow{4}{*}{Dataset Quality} & Contains spelling errors such as “levls”, “paramaters”, “reference”, “adress”, “refining”, “ponters”, and “perenthesis”. Some answers are too short to assess for keyword extraction. There may also be other quality factors that affect the dataset, such as inconsistent formatting or grammar errors. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the [PERSON] dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Keyword** & **Gram** & **YAKE Score** & **RAKE Score** & **KeyBERT Score** \\ \hline software & 1 & 0.090 & 0.040 & 1.00 \\ product & 1 & 0.130 & 0.134 & 1.00 \\ simulate & 1 & 0.148 & 0.243 & 0.760 \\ behavior & 1 & 0.137 & 0.321 & 0.806 \\ desired & 1 & 0.106 & 0.110 & 0.765 \\ portions & 1 & 0.125 & 0.102 & 0.630 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: YAKE, RAKE, and KeyBERT on the [PERSON] dataset with one gram. and KeyBERT, respectively. In this particular example, KeyBERT provided the highest scores for all keywords, with a score of one assigned to \"software product\" and \"product\", indicating that these words are the most important and relevant in the given text.