創新命名實體識別模型應用於專業化序列標記

A Novel Named Entity Recognition Model Applied to Specialized Sequence Labeling

近來，序列分割和標記的需求已經劃分了不同的專業領域。在傳統解決方案中，最常用的模型是結合了深度學習和監督學習的群體長短期記憶條件隨機場（Bi-LSTM-CRF），由於無監督學習的重要性已與監督學習並駕齊驅，本研究提出了長短期記憶-無監督監督學習-一般條件隨機場（Bi-LSTM-USLGRF）模型，將通用隨機條件場（GRF）與無監督監督學習（USL）和Bi-LSTM相結合，實現了我們監督學習、無監督學習和深度學習的概念性結合。在本研究中，提供了一種創新的GRF架構來取代傳統的CRF架構，以及將無監督學習與有監督學習相結合的USL原理。我們證明，該模型不僅展示了利用USL原理的專業能力，還具有GRF的特殊優勢，其性能優於之前的Bi-LSTM-CRF架構提高了1.45%。所提出的USL和GRF的組合具有更大的靈活性，未來甚至可以在不同的領域得到應用和推廣。

The demand for sequence segmentation and tagging has recently extended to different professional fields. The most commonly used model in conventional solutions is Bidirectional Long Short-Term Memory-Conditional Random Fields (Bi- LSTM-CRF), which combines deep learning and supervised learning. As the importance of unsupervised learning has become equal to that of supervised learning, this study proposes a Bidirectional Long Short-term Memory-Unsupervised Supervised Learning-General Conditional Random Field (Bi-LSTM-USL-GRF) model that combines General Conditional Random Field (GRF) with Unsupervised Supervised Learning (USL) and Bi-LSTM, achieving a conceptual combination of supervised learning, unsupervised learning, and deep learning. In this study, we provide an innovative GRF architecture to replace the traditional CRF architecture, as well as the USL principle, which combines unsupervised learning with supervised learning. We demonstrate that this model not only demonstrates specialized ability in the use of the USL principle but also has the special advantages of GRF, outperforming the previous Bi-LSTM-CRF architecture with a performance improvement of 1.45%. The proposed USL and GRF has more flexibility in its combination and could even be used and promoted in different fields.