臺灣博碩士論文加值系統

自傳是在人力資源管理中重要的文件之一，它提供了應聘者的個人資訊和經歷，用於招聘選拔過程。然而，由於自傳通常是非結構化的文本，提取其中的關鍵資訊對於人力資源部門來說是一項具有挑戰性的任務。因此，研究人員開始探索使用自然語言處理技術來從自傳中提取資訊，以提高招聘流程的效率和有效性。
本研究的動機在於傳統的人力資源管理方法偏向客觀條件的評估，忽略了個人特質對於職位適配度的重要性。因此，我們希望通過深度學習模型來進行自傳文本的識別任務，以更全面地了解應聘者的能力和適應能力。過去的研究主要集中在自然語言處理的各個領域，如SpaCy、問答任務和語句分類任務。這些研究為我們提供了實現自傳文本識別的基礎。在本研究中，我們使用了機器學習與深度學習模型，進行了一系列的實驗和分析。
實驗結果表明，BERT模型在自傳文本的序列標注任務中表現出優越性能，在自傳處理中的序列標注任務中具有潛力和應用價值，並在Precision、Recall和F1-score等評估指標上取得較好的表現。此外，我們也觀察到審查項目標籤的特徵、描述方式和訓練資料量對BERT模型的預測結果有重要影響。

An autobiography is one of the crucial documents in human resource management as it provides personal information and experiences of applicants for the recruitment and selection process. However, extracting key information from autobiographies can be a challenging task for the human resources department due to their typically unstructured nature. Consequently, researchers have begun exploring the use of natural language processing techniques to extract information from autobiographies, aiming to enhance the efficiency and effectiveness of the recruitment process.
The motivation behind this study stems from the fact that traditional human resource management methods tend to prioritize evaluating objective criteria while overlooking the significance of individual traits in job suitability. Hence, our objective is to utilize deep learning models for the task of identifying autobiographical texts to gain a more comprehensive understanding of applicants' abilities and adaptability.Previous research has primarily focused on various aspects of natural language processing, including SpaCy, question-answering tasks, and sentence classification tasks. These studies lay the foundation for implementing the identification of autobiographical texts. In this study, we conducted a series of experiments and analyses employing machine learning and deep learning models.
The experimental results demonstrate that the BERT model exhibits superior performance in the task of span recognition for autobiographical texts. It shows potential and practical value in sequence tagging tasks for autobiographical processing, achieving better performance in evaluation metrics such as Precision, Recall, and F1-score. Additionally, we observed that the characteristics, description methods, and training data size of review item labels significantly influence the prediction results of the BERT model.

誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究目的 5
第四節 研究流程 6
第二章 文獻探討 7
第一節 人資部門審查履歷的關鍵因素 7
一. 在招聘審查中提取履歷的關鍵因素 7
二. 定義履歷中的關鍵因素 8
三. 履歷關鍵因素中技能和特質的重要 8
第二節 自然語言處理 10
一. 條件隨機場與隱藏馬可夫模型 10
二. 自定義的SpaCy 模型 11
三. 序列標注任務、問答任務、語句分類任務 11
第三節 自然語言處理在人力資源審查中的應用 14
一. 履歷的複雜結構應用 14
二. 履歷中的相關關鍵詞和信息應用 15
第三章 研究方法 17
第一節 審查項目標籤 18
第二節 標記準則與過程 21
第三節 模型架構 23
一. 特徵萃取 23
二. 序列標注任務 24
三. 問答任務 25
四. 語句分類任務 25
第四節 模型評估 26
第四章 研究結果與分析 28
第一節 資料收集 28
第二節 資料整理 29
第三節 實驗設計 34
一. 實驗項目 34
二. 超參數調整 35
第四節 實驗結果 36
一. 超參數調整效果比較 36
二. 模型辨識效果比較 43
三. 各審查項目標籤跨度辨識效果比較 46
四. 審查項目標籤B、I、L、U辨識效果比較 48
五. 審查項目標籤長短的辨識效果比較 50
第五節 討論與管理意涵 52
第五章 結論 54
第一節 研究發現 54
第二節 限制與未來展望 55
參考文獻 56
附錄一 BERT序列標注任務最佳參數組合效果 60
附錄二 BERT序列標注任務BILU tag效果 61
附錄三 BERT的問答任務最佳參數組合效果 62
附錄四 BERT的問答任務BILU tag效果 63
附錄五 BERT語句分類任務效果 64
附錄六 SpaCy序列標注任務效果 65
附錄七 SpaCy序列標注任務BILU tag效果 66
附錄八 CRF序列標注任務最佳參數組合效果 67
附錄九 CRF最佳參數組合BILU tag效果 68
附錄十 RFC的序列標注任務最佳參數組合效果 69
附錄十一 RFC的序列標注任務BILU tag效果 70
附錄十二 HMM的序列標注任務效果 71
附錄十三 HMM的序列標注任務BILU tag效果 72

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