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17th International Conference on World Wide Web 2008, WWW'08

作者： Zhiyuan, Liu Maosong, Sun Department of Computer Sci. and Tech. State Key Lab. on Intelligent Tech. and Sys. Tsinghua University Beijing 100084 China State Key Lab. on Intelligent Tech. and Sys. National Lab for Information Sci. and Tech. Tsinghua University Beijing 100084 China

ISBN: (纸本)9781605580852

This paper presents a new query recommendation method that generates recommended query list by mining large-scale user logs. Starting from the user logs of click-through data, we construct a bipartite network where the nodes on one side correspond to unique queries, on the other side to unique URLs, Inspired by the bipartite network based resource allocation method, we try to extract the hidden information from the Query-URL bipartite network. The recommended queries generated by the method are asymmetrical which means two related queries may have different strength to recommend each other. To evaluate the method, we use one week user logs from Chinese search engine Sogou. The method is not only 'content ignorant', but also can be easily implemented in a paralleled manner, which is feasible for commercial search engines to handle large scale user logs.

关键词： Search engines

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Classifying what-type questions by head noun tagging

Classifying what-type questions by head noun tagging

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22nd International Conference on Computational Linguistics, Coling 2008

作者： Li, Fangtao Zhang, Xian Yuan, Jinhui Zhu, Xiaoyan State Key Laboratory on Intelligent Technology and Systems Tsinghua Department of Computer Sci. and Tech. Tsinghua University Beijing 100084 China

ISBN: (纸本)9781905593446

Classifying what-type questions into proper semantic categories is found more challenging than classifying other types in question answering systems. In this paper, we propose to classify what-type questions by head noun tagging. The approach highlights the role of head nouns as the category discriminator of what-type questions. To reduce the semantic ambiguities of head noun, we integrate local syntactic feature, semantic feature and category dependency among adjacent nouns with Conditional Random Fields (CRFs). Experiments on standard question classification data set show that the approach achieves state-of-the-art performances. © 2008. Licensed under the Creative Commons.

关键词： Classification (of information)

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Sentiment vector space model for lyric-based song sentiment classification

Sentiment vector space model for lyric-based song sentiment ...

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46th Annual Meeting of the Association for Computational Linguistics: Human Language tech.ologies, ACL-08: HLT

作者： Xia, Yunqing Wang, Linlin Wong, Kam-Fai Xu, Mingxing Center for Speech and Language Tech. RIIT Tsinghua University Beijing 100084 China Dept. of CST State Key Lab of Intelligent Tech. and Sys. Tsinghua University Beijing 100084 China Dept. of SE and EM Chinese University of Hong Kong Shatin Hong Kong Dept. of CST Tsinghua University Beijing 100084 China

ISBN: (纸本)9781932432046

Lyric-based song sentiment classification seeks to assign songs appropriate sentiment labels such as light-hearted and heavy-hearted. Four problems render vector space model (VSM)-based text classification approach ineffective: 1) Many words within song lyrics actually contribute little to sentiment;2) Nouns and verbs used to express sentiment are ambiguous;3) Negations and modifiers around the sentiment keywords make particular contributions to sentiment;4) Song lyric is usually very short. To address these problems, the sentiment vector space model (s-VSM) is proposed to represent song lyric document. The preliminary experiments prove that the s- VSM model outperforms the VSM model in the lyric-based song sentiment classification task. © 2008 Association for Computational Linguistics.

关键词： Vector spaces

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Web Image Clustering Based on Multi-instance

Web Image Clustering Based on Multi-instance

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jing Lu ShaoPing Ma Min Zhang State Key Laboratory of Intelligent Tech. & Systems Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Beijing China

In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi-instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific Web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.

关键词： Image retrieval Clustering algorithms intelligent agent Partitioning algorithms Supervised learning Iterative algorithms Content based retrieval intelligent systems Information science Computer science

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Sentiment vector space model for lyric-based song sentiment classification 46

Sentiment vector space model for lyric-based song sentiment ...

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46th Annual Meeting of the Association for Computational Linguistics: Human Language tech.ologies, ACL 2008

作者： Xia, Yunqing Wong, Kam-Fai Wang, Linlin Xu, Mingxing Center for Speech and language Tech RIIT Tsinghua University Beijing100084 China Dept. of SE&EM The Chinese University of Hong Kong Shatin Hong Kong State Key Lab. of Intelligent Tech. and Sys. Dept. of CST Tsinghua University Beijing100084 China Dept. of CST Tsinghua University Beijing100084 China

Lyric-based song sentiment classification seeks to assign songs appropriate sentiment labels such as light-hearted and heavy-hearted. Four problems render vector space model (VSM)-based text classification approach ineffective: 1) Many words within song lyrics actually contribute little to sentiment;2) Nouns and verbs used to express sentiment are ambiguous;3) Negations and modifiers around the sentiment keywords make particular contributions to sentiment;4) Song lyric is usually very short. To address these problems, the sentiment vector space model (s-VSM) is proposed to represent song lyric document. The preliminary experiments prove that the s-VSM model outperforms the VSM model in the lyric-based song sentiment classification task. © 2008 Association for Computational Linguistics.

关键词： Vector spaces

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Video search in concept subspace: A text-like paradigm 07

Video search in concept subspace: A text-like paradigm

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6th ACM International Conference on Image and Video Retrieval, CIVR 2007

作者： Li, Xirong Wang, Dong Li, Jianmin Zhang, Bo State Key Lab. of Intelligent Tech. and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)1595937331

Though both quantity and quality of semantic concept detection in video are continuously improving, it still remains unclear how to exploit these detected concepts as semantic indices in video search, given a specific query. In this paper, we tackle this problem and propose a video search framework which operates like searching text documents. Noteworthy for its adoption of the well-founded text search principles, this framework first selects a few related concepts for a given query, by employing a tf-idf like scheme, called c-tf-idf, to measure the informativeness of the concepts to this query. These selected concepts form a concept subspace. Then search can be conducted in this concept subspace, either by a Vector Model or a Language Model. Further, two algorithms, i.e., Linear Summation and Random Walk through Concept-Link, are explored to combine the concept search results and other baseline search results in a reranking scheme. This framework is both effective and efficient. Using a lexicon of 311 concepts from the LSCOM concept ontology, experiments conducted on the TRECVID 2006 search data set show that: when used solely, search within the concept subspace achieves the state-of-the-art concept search result;when used to rerank the baseline results, it can improve over the top 20 automatic search runs in TRECVID 2006 on average by approx. 20%, on the most significant one by approx. 50%, all within 180 milliseconds on a normal PC. Copyright 2007 ACM.

关键词： Semantics

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Video diver: Generic video indexing with diverse features

Video diver: Generic video indexing with diverse features

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International Multimedia Conference, MM'07 - 9th ACM SIG Multimedia International Workshop on Multimedia Information Retrieval, MIR'07

作者： Wang, Dong Liu, Xiaobing Luo, Linjie Li, Jianmin Zhang, Bo State Key Lab. of Intelligent Tech. and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781595937780

Semantic video indexing is critical for practical video retrieval systems and a generic and scalable indexing framework is a must for indexing a large semantic lexicon with over 1000 concepts present. This paper fully explores the idea of incorporating many kinds of diverse features into a single framework, combining them altogether to obtain larger degree of invariance which is absent in any of the component features, and thus achieves genericness and scalability. We scale down the formidable computational expense with a clever design of the classification and fusion schemes. To be specific, ~20 kinds of diverse features are extracted to capture limited yet complementary variance in color, texture and edge with spatial constraints implicitly integrated, and over 100 classifiers are built subsequently and fused to produce a generic detector. The extensive experiments on a total of 310 hours of TRECVID news videos show that the proposed framework yields significantly improved performance over that of the best single feature across a variety of concepts. Moreover, a benchmark comparison demonstrates that this approach is state-of-the-art. Meanwhile, the proposed approach generalizes well over previously unseen programs and stations and scales well to a lexicon of over 300 concepts in the LSCOM [18] ontology. Copyright 2007 ACM.

关键词： Indexing (of information)

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The importance of query-concept-mapping for automatic video retrieval

The importance of query-concept-mapping for automatic video ...

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15th ACM International Conference on Multimedia, MM'07

作者： Wang, Dong Li, Xirong Li, Jianmin Zhang, Bo State Key Lab. of Intelligent Tech. and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781595937025

A new video retrieval paradigm of query-by-concept emerges recently. However, it remains unclear how to exploit the detected concepts in retrieval given a multimedia query. In this paper, we point out that it is important to map the query to a few relevant concepts instead of search with all concepts. In addition, we show that solving this problem through both text and image inputs are effective for search, and it is possible to determine the number of related concepts by a language modeling approach. Experimental evidence is obtained on the automatic search task of TRECVID 2006 using a large lexicon of 311 learned semantic concept detectors. Copyright 2007 ACM.

关键词： Image retrieval

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SAR image ATR using SVM with a low dimensional combined feature

SAR image ATR using SVM with a low dimensional combined feat...

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Wang, Hongqiao Sun, Fuchun Zhao, Zongtao Cai, Yanning Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Systems Beijing 100084 Peoples R China Xian Res Inst Hi Tech Command Automat Dept Xian Peoples R China

ISBN: (纸本)9780819469502

In this paper, a grayscale wavelet moment and entropy combined feature, which can well represent the images with much lower dimensions, is proposed for the MSTAR targets' classification, also a discriminative feature selection and evaluation method for multi-class targets is presented. By introducing SVM as the classifier to some simulation tests, it can be shown that the grayscale wavelet moment and entropy combined feature has good capability for translation, scaling and rotation transformation in both local information and global information conditions, by the reasons of wavelet moments' multi-resolution analysis, moment invariant quality and entropy's statistic quality for image disorder. The test also confirms that this feature has a significant improvement on classificatory accuracy using SVM with a lower feature dimension than the other features such as the single wavelet moment, Hu's moment and PCA.

关键词： SAR ATR MSTAR wavelet moment entropy SVM PCA

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Regularized clustering for documents

Proceedings of the 30th Annual International ACM SIGIR Confe...

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Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07 2007年 95-102页

作者： Wang, Fei Zhang, Changshui Li, Tao State Key Lab of Intelligent Tech. and Systems Department of Automation Tsinghua University Beijing 100084 China School of Computer Science Florida International University Miami FL 33199 United States

ISBN: (纸本)1595935975

In recent years, document clustering has been receiving more and more attentions as an important and fundamental tech.ique for unsupervised document organization, automatictopic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine all those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods. Copyright 2007 ACM.

关键词： Information retrieval

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