tailieunhanh - Báo cáo khoa học: "The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language"

In this paper, we present initial experiments in the recognition of deceptive language. We introduce three data sets of true and lying texts collected for this purpose, and we show that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language. We also introduce a method for class-based feature analysis, which sheds some light on the features that are characteristic for deceptive text. | The Lie Detector Explorations in the Automatic Recognition of Deceptive Language Rada Mihalcea University of North Texas rada@ Carlo Strapparava FBK-IRST strappa@ Abstract In this paper we present initial experiments in the recognition of deceptive language. We introduce three data sets of true and lying texts collected for this purpose and we show that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language. We also introduce a method for class-based feature analysis which sheds some light on the features that are characteristic for deceptive text. You should not trust the devil even if he tells the truth. Thomas of Aquin medievalphilosopher 1 Introduction and Motivation The discrimination between truth and falsehood has received significant attention from fields as diverse as philosophy psychology and sociology. Recent advances in computational linguistics motivate us to approach the recognition of deceptive language from a data-driven perspective and attempt to identify the salient features of lying texts using natural language processing techniques. In this paper we explore the applicability of computational approaches to the recognition of deceptive language. In particular we investigate whether automatic classification techniques represent a viable approach to distinguish between truth and lies as expressed in written text. Although acoustic and other non-linguistic features were also found to be useful for this task Hirschberg et al. 2005 we deliberately focus on written language since it represents the type of data most frequently encountered on the Web . chats forums or in other collections of documents. Specifically we try to answer the following two questions. First are truthful and lying texts separable and does this property hold for different datasets To answer this question we use three different data sets that we construct for this purpose - consisting of true and false .

• Báo cáo khoa học
• The Lie Detector
• Explorations in the Automatic Recognition
• Deceptive Language
• 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