TAILIEUCHUNG - Báo cáo khoa học: "Unsupervised Multilingual Learning for Morphological Segmentation"

For centuries, the deep connection between languages has brought about major discoveries about human communication. In this paper we investigate how this powerful source of information can be exploited for unsupervised language learning. In particular, we study the task of morphological segmentation of multiple languages. We present a nonparametric Bayesian model that jointly induces morpheme segmentations of each language under consideration and at the same time identifies cross-lingual morpheme patterns, or abstract morphemes. . | Unsupervised Multilingual Learning for Morphological Segmentation Benjamin Snyder and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology bsnyder regina @ Abstract For centuries the deep connection between languages has brought about major discoveries about human communication. In this paper we investigate how this powerful source of information can be exploited for unsupervised language learning. In particular we study the task of morphological segmentation of multiple languages. We present a nonparametric Bayesian model that jointly induces morpheme segmentations of each language under consideration and at the same time identifies cross-lingual morpheme patterns or abstract morphemes. We apply our model to three Semitic languages Arabic Hebrew Aramaic as well as to English. Our results demonstrate that learning morphological models in tandem reduces error by up to 24 relative to monolingual models. Furthermore we provide evidence that our joint model achieves better performance when applied to languages from the same family. 1 Introduction For centuries the deep connection between human languages has fascinated linguists anthropologists and historians Eco 1995 . The study of this connection has made possible major discoveries about human communication it has revealed the evolution of languages facilitated the reconstruction of proto-languages and led to understanding language universals. The connection between languages should be a powerful source of information for automatic linguistic analysis as well. In this paper we investigate two questions i Can we exploit cross-lingual correspondences to improve unsupervised language learning ii Will this joint analysis provide more or less benefit when the languages belong to the same family We study these two questions in the context of unsupervised morphological segmentation the automatic division of a word into morphemes the basic units of meaning

• Báo cáo khoa học
• Unsupervised Multilingual Learning
• Morphological Segmentation
• Benjamin Snyder and Regina Barzilay
• 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