Broadcast News Story Segmentation Using Conditional Random Fields and Multimodal Features
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- WANG Xiaoxuan
- School of Computer Science, Northwestern Polytechnical University
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- XIE Lei
- School of Computer Science, Northwestern Polytechnical University
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- LU Mimi
- School of Computer Science, Northwestern Polytechnical University
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- MA Bin
- Institute for Infocomm Research
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- CHNG Eng Siong
- School of Computer Engineering, Nanyang Technological University
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- LI Haizhou
- Institute for Infocomm Research
Bibliographic Information
- Other Title
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- セレクト及びマージ頂点数の最小化によるパイプライン化依存性グラフの簡単化
- セレクト オヨビ マージ チョウテンスウ ノ サイショウカ ニ ヨル パイプラインカ イソンセイ グラフ ノ カンタンカ
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Abstract
In this paper, we propose integration of multimodal features using conditional random fields (CRFs) for the segmentation of broadcast news stories. We study story boundary cues from lexical, audio and video modalities, where lexical features consist of lexical similarity, chain strength and overall cohesiveness; acoustic features involve pause duration, pitch, speaker change and audio event type; and visual features contain shot boundaries, anchor faces and news title captions. These features are extracted in a sequence of boundary candidate positions in the broadcast news. A linear-chain CRF is used to detect each candidate as boundary/non-boundary tags based on the multimodal features. Important interlabel relations and contextual feature information are effectively captured by the sequential learning framework of CRFs. Story segmentation experiments show that the CRF approach outperforms other popular classifiers, including decision trees (DTs), Bayesian networks (BNs), naive Bayesian classifiers (NBs), multilayer perception (MLP), support vector machines (SVMs) and maximum entropy (ME) classifiers.
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E95.D (5), 1206-1215, 2012
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390282679354417024
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- NII Article ID
- 10030942437
- 110009444737
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- NII Book ID
- AA12099634
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- ISSN
- 18804535
- 17451361
- 09168532
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- BIBCODE
- 2012IEITI..95.1206W
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- NDL BIB ID
- 023751202
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed