TAILIEUCHUNG - Báo cáo khoa học: "Multi-Task Active Learning for Linguistic Annotations"

We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols, alternating selection and rank combination, and propose a method to implement them in practice. We experiment with a twotask annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. . | Multi-Task Active Learning for Linguistic Annotations Roi Reichart1 Katrin Tomanek2 Udo Hahn2 Ari Rappoport1 institute of Computer Science Hebrew University of Jerusalem Israel roiri arir @ Jena University Language Information Engineering Julie Lab Friedrich-Schiller-Universitat Jena Germany @ Abstract We extend the classical single-task active learning AL approach. In the multi-task active learning MTAL paradigm we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols alternating selection and rank combination and propose a method to implement them in practice. We experiment with a two-task annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. MTAL outperforms random selection and a stronger baseline onesided example selection in which one task is pursued using AL and the selected examples are provided also to the other task. 1 Introduction Supervised machine learning methods have successfully been applied to many NLP tasks in the last few decades. These techniques have demonstrated their superiority over both hand-crafted rules and unsupervised learning approaches. However they require large amounts of labeled training data for every level of linguistic processing . POS tags parse trees or named entities . When when domains and text genres change . moving from commonsense newspapers to scientific biology journal articles extensive retraining on newly supplied training material is often required since different domains may use different syntactic structures as well as different semantic classes entities and relations . Both authors contributed equally to this work. Consequently with an increasing coverage of a wide variety of domains in human language technology HLT systems we can expect a growing need for manual annotations to support many kinds of .

• Báo cáo khoa học
• Multi Task Active Learning
• Linguistic Annotations
• Roi Reichart
• 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