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| Title: | 生命專線對談自適應重點萃取進行自殺意念分析
Self-Adapted Utterance Selection for Suicidal Ideation Analysis in Lifeline Conversations |
| Authors: | 王中伶
Wang, Zhong-Ling |
| Contributors: | 黃瀚萱
Huang, Hen-Hsen
王中伶
Wang, Zhong-Ling |
| Keywords: | 生命專線
自殺意念偵測
自適應重點萃取
對話理解
自然語言處理
Lifeline
Suicidal Ideation Detection
Self-Adapted Utterance Selection
Conversation Understanding
Natural Language Processing |
| Date: | 2022 |
| Issue Date: | 2022-09-02 15:05:19 (UTC+8) |
| Abstract: | 近年來，心理健康逐漸受到重視，尤其致命性的自殺議題更得到關注，臺灣安心專線針對該議題提供民眾免費撥打服務，透過通話方式給予來電者心理方面的建議及協助，本論文進而透過對談內容，進行自殺意念風險分析。
本論文之資料集來自安心專線真實個案，由心理專業團隊聽寫成文本，再經專家依照個案的自殺意念狀況進行分類。由於社工與來電者的對談內容冗長又充滿雜訊，不利機器學習模型預測，因此，基於自然語言處理技術，本論文提出自適應萃取方法，將對談中擁有重要特徵及資訊的句子萃取出來並將其串接，再利用該縮減內容，預測自殺意念風險。實驗結果顯示，本方法於各風險類別得出最高效能，且被萃取出來的句子得以進行可解釋性的語意分析。此外，針對自殺防治，以提早偵測任務於各對談上進行測試，期望在對談中，能越早發現來電者的需求並及時給予適當的資源，降低社工的負擔。
最後，除了自殺議題之外，我們希望將本方法廣泛應用至不同領域，達成重點內容萃取、資料長度縮減，進而提升效能且更有效率地進行語意分析，因此，以航空客服及電影影評資料集進行實驗，且驗證本方法適合的使用情境。
Our work investigates an important issue in mental healthcare, suicidal ideation detection in the phone-call conversations of Taiwan Lifeline. The conversation between the caller and the counsellor is often long, noisy, and covering diverse topics, making the model challenged to classify the suicidal ideation of the caller. To facilitate the NLP model for suicidal ideation detection, we propose a novel self-adapted approach that aims to select the critical utterances that are easier for the underlying NLP model to discriminate. The real-world Lifeline transcriptions labeled by experts are adopted in experiments. Experimental results show the effectiveness of our approach in overall performance improvement. The selected utterances can also be regarded as explanation information. The early detection is effective for our study of suicide prevention. Not limited to the healthcare domain, our approach is applied to the flight booking state classification on the AirDialogue dataset and sentiment binary classification on IMDb and Polarity datasets to explore the suitable scenario for general applications. |
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| Description: | 碩士
國立政治大學
資訊科學系
109753106 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109753106 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201360 |
| Appears in Collections: | [資訊科學系] 學位論文
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