TAILIEUCHUNG - Báo cáo khoa học: "Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction"

Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. . | Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction Jing Jiang School of Information Systems Singapore Management University 80 Stamford Road Singapore 178902 jingjiang@ Abstract Creating labeled training data for relation extraction is expensive. In this paper we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. The proposed framework models the commonality among different relation types through a shared weight vector enables knowledge learned from the auxiliary relation types to be transferred to the target relation type and allows easy control of the tradeoff between precision and recall. Empirical evaluation on the ACE 2004 data set shows that the proposed method substantially improves over two baseline methods. 1 Introduction Relation extraction is the task of detecting and characterizing semantic relations between entities from free text. Recent work on relation extraction has shown that supervised machine learning coupled with intelligent feature engineering or kernel design provides state-of-the-art solutions to the problem Culotta and Sorensen 2004 Zhou et al. 2005 Bunescu and Mooney 2005 Qian et al. 2008 . However supervised learning heavily relies on a sufficient amount of labeled data for training which is not always available in practice due to the labor-intensive nature of human annotation. This problem is especially serious for relation ex traction because the types of relations to be extracted are highly dependent on the application domain. For example when working in the financial domain we may be interested in the employment relation

• Báo cáo khoa học
• Multi Task Transfer Learning
• Weakly Supervised Relation Extraction
• Jing Jiang
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học