tailieunhanh - Báo cáo khoa học: "Unsupervised Topic Modelling for Multi-Party Spoken Discourse"

We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al., 2003) while simultaneously extracting topics which rate highly when assessed for coherence by human judges. . | Unsupervised Topic Modelling for Multi-Party Spoken Discourse Matthew Purver CSLI Stanford University Stanford CA 94305 UsA mpurver@ Thomas L. Griffiths Dept. of Cognitive Linguistic Sciences Brown University Providence RI 02912 USA tomgriffiths@ Abstract We present a method for unsupervised topic modelling which adapts methods used in document classification Blei et al. 2003 Griffiths and Steyvers 2004 to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods Galley et al. 2003 while simultaneously extracting topics which rate highly when assessed for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors. 1 Introduction Topic segmentation - division of a text or discourse into topically coherent segments - and topic identification - classification of those segments by subject matter - are joint problems. Both are necessary steps in automatic indexing retrieval and summarization from large datasets whether spoken or written. Both have received significant attention in the past see Section 2 but most approaches have been targeted at either text or monologue and most address only one of the two issues usually for the very good reason that the dataset itself provides the other for example by the explicit separation of individual documents or news stories in a collection . Spoken multi-party meetings pose a difficult problem firstly neither the Konrad P. Kording Dept. of Brain Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA kording@ Joshua B. Tenenbaum Dept. of Brain Cognitive Sciences Massachusetts Institute of .

• Báo cáo khoa học
• Unsupervised Topic Modelling
• Multi Party Spoken Discourse
• Matthew Purver
• báo cáo khoa học
• mô hình 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 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