## 4 Discussion
However, in contrast to the intent classification result, a high F1-score was derived when the FastText augmentation method was applied. The accuracy of the EDM method tended to decrease as the number of augmentations increased but the FastText method improved accuracy. The named entity ACTION can change the word \"\(\mathfrak{n}\mathfrak{n}\mathfrak{n}\mathfrak{n}\mathfrak{n}\mathfrak{n} \mathfrak{n}\mathfrak{n}\ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Line** & **Speaker** & **Text** & **Classified Intent, Recognized Entity** & **NHCID Adop Question** \\ \hline
1 & Chatbot & As far as I know, something happened to you, can you tell me everything about what happened to you, from beginning to end? & & Invitation \\ \hline
2 & Child & Yesterday, I was going home with my friend after school, and an old man came to me to teach me how to use my cell phone and touched my body. & ID, Yesterday(WHEN), old man(WHO), touched(ACTION) & \\ \hline
3 & Chatbot & Please elaborate a little more on what you just told me. & & Invitation \\ \hline
4 & Child & I was with a friend, and he came to me. That’s why he asked me to teach him how to capture it. So, he asked me to help him, so I helped him, but he suddenly grabbed my waist. & ID, old man(WHO), grabbed(ACTION) & \\ \hline
5 & Chatbot & Then I’d like to ask you about the things that you told me in detail. & & Invitation \\ \hline
6 & Chatbot & Please tell me more about that time. & & Cued-invitation \\ \hline
7 & Child & It is probably 3 o’clock because it is time to go home after school. & 3 o’clock (WHEN) & \\ \hline
8 & Chatbot & Please tell me more about the position you were in at the time. & & Option-posing \\ \hline
9 & Child & At that time, I was turning my back on the apartment, and the old man came to us after sitting on a bench. & ID, At that time(WHEN) & \\ \hline
10 & Chatbot & Please tell me everything you know about him. & & Cued-invitation \\ \hline
11 & Child & He was wearing a hat, so I can’t remember his face in detail... He was wearing something like a coat, and he was lame. & ALM, he(WHO) & \\ \hline
12 & Chatbot & Could you tell me what action you took in detail when that person “touched” you? & & Cued-invitation \\ \hline
13 & Child & I felt that he was trying to touch my body with force. So, I was so embarrassed that I shook off his hand and ran away with my friend because I was scared. & SPD & \\ \hline
14 & Chatbot & You told me that he “grabbed” it, but could you tell me in more detail about it? & & Cued-invitation \\ \hline
15 & Child & He pushed his face closer to me asking for instructions on how to capture a cell phone and then I pretended to be natural and put my hands around my waist. & ID & \\ \hline
16 & Chatbot & Could you tell me how you felt when it happened? & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Conversation table excerpted from a part of the incident-related investigation with a chatbot. The gray rows are the chatbot’s responses or questions, and the white rows are the user’s responses. In actual statement, words representing the time of the crime and perpetrator were not as diverse as the ACTION entity. For example, most words only denoted a specific range, such as \"Monday\" to \"Sunday\", \"01:00\" to \"24:00\", \"morning\", \"evening\", \"night\", \"midnight\", \"he\", \"father\", \"grandfather\", and \"maternal grandfather\". However, since the WHEN and WHO entities that recognize the primary information of an incident are significant when gathering testimonies, we set them as entities to be recognized despite their small scope of expression. Subsequently, during the augmentation process, the expressions of the WHEN and WHO entities presented in the sentence were further trained. Unlike other entities, they achieved a relatively high F1-score of 100 compared with the recognition result of the original data. According to the analysis results, the best combination of intent classification and NER for collecting sentences is KoBERT-EDA20 for intent classification and KoBERT-FastText30 model for NER.