OpenRaiser/Intern-Atlas huggingface dataset:
Adding structure to top-k: from items to expansions
Authors: Xueyao Liang, Min Xie, Laks V. S. Lakshmanan
Venue: CIKM
Year: 2011

Keyword based search interfaces are extremely popular as a means for efficiently discovering items of interest from a huge collection, as evidenced by the success of search engines like Google and Bing. However, most of the current search services still return results as a flat ranked list of items. Considering the huge number of items which can match a query, this list based interface can be very difficult for the user to explore and find important items relevant to their search needs. In this work, we consider a search scenario in which each item is annotated with a set of keywords. E.g., in Web 2.0 enabled systems such as flickr and del.icio.us, it is common for users to tag items with keywords. Based on this annotation information, we can automatically group query result items into different expansions of the query corresponding to subsets of keywords. We formulate and motivate this problem within a top-k query processing framework, but as that of finding the top-k most important expansions. Then we study additional desirable properties for the set of expansions returned, and formulate the problem as an optimization problem of finding the best k expansions satisfying all the desirable properties. We propose several efficient algorithms for this problem. Our problem is similar in spirit to recent works on automatic facets generation, but has the important difference and advantage that we don't need to assume the existence of pre-defined categorical hierarchy which is critical for these works. Through extensive experiments on both real and synthetic datasets, we show our proposed algorithms are both effective and efficient.
KLEAP: an efficient cleaning method to remove cross-reads in RFID streams
Authors: Guoqiong Liao, Jing Li, Lei Chen, Changxuan Wan
Venue: CIKM
Year: 2011

Recently, the RFID technology has been widely used in many kinds of applications. However, because of the interference from environmental factors and limitations of the radio frequency technology, the data streams collected by the RFID readers are usually contain a lot of cross-reads. To address this issue, we propose a KerneL dEnsity-bAsed Probability cleaning method (KLEAP) to remove cross-reads within a sliding window. The method estimates the density of each tag using a kernel-based function. The reader corresponding to the micro-cluster with the largest density will be regarded as the position that the tagged object should locate in current window, and the readings derived from other readers will be treated as the cross-reads. Experiments verify the effectiveness and efficiency of the proposed method.
Query sampling for learning data fusion
Authors: Ting-Chu Lin, Pu-Jen Cheng
Venue: CIKM
Year: 2011

Data fusion is to merge the results of multiple independent retrieval models into a single ranked list. Several earlier studies have shown that the combination of different models can improve the retrieval performance better than using any of the individual models. Although many promising results have been given by supervised fusion methods, training data sampling has attracted little attention in previous work of data fusion. By observing some evaluations on TREC and NTCIR datasets, we found that the performance of one model varied largely from one training example to another, so that not all training examples were equivalently effective. In this paper, we propose two novel approaches: greedy and boosting approaches, which select effective training data by query sampling to improve the performance of supervised data fusion algorithms such as BayesFuse, probFuse and MAPFuse. Extensive experiments were conducted on five data sets including TREC-3,4,5 and NTCIR-3,4. The results show that our sampling approaches can significantly improve the retrieval performance of those data fusion methods.
Learning to rank with cross entropy
Authors: Yuan Lin, Hongfei Lin, Jiajin Wu, Kan Xu
Venue: CIKM
Year: 2011

Learning to rank algorithms are usually grouped into three types: the point wise approach, the pairwise approach, and the listwise approach, according to the input spaces. Much of the prior work is based on the three approaches to learn the ranking model to predict the relevance of a document to a query. In this paper, we focus on the problem of constructing new input space based on groups of documents with the same relevance judgment. A novel approach is proposed based on cross entropy to improve the existing ranking method. The experimental results show that our approach leads to significant improvements in retrieval effectiveness.
Language-independent sentiment classification using three common words
Authors: Zheng Lin, Songbo Tan, Xueqi Cheng
Venue: CIKM
Year: 2011

Many methods for cross-lingual processing tasks are resource-dependent, which will not work without machine translation system or bilingual lexicon. In this paper, we propose a novel approach for multilingual sentiment classification just by few seed words. For a given language, the proposed approach learns a sentiment classifier from the initial seed words instead of any labeled data. We employ our method both in supervised learning and unsupervised learning. Experimental results demonstrate that our method relies less on external resource but performs as well as or better than the baseline.
Authentication of location-based skyline queries
Authors: Xin Lin, Jianliang Xu, Haibo Hu
Venue: CIKM
Year: 2011

In outsourced spatial databases, the location-based service (LBS) provides query services to the clients on behalf of the data owner. However, if the LBS is not trustworthy, it may return incorrect or incomplete query results. Thus, authentication is needed to verify the soundness and completeness of query results. In this paper, we study the authentication problem for location-based skyline queries, which have recently been receiving increasing attention in LBS applications. We propose two authentication methods: one based on the traditional MR-tree index and the other based on a newly developed MR-Sky-tree. Experimental results demonstrate the efficiency of our proposed methods in terms of the authentication cost.
Learning conditional random fields with latent sparse features for acronym expansion finding
Authors: Jie Liu, Jimeng Chen, Yi Zhang, Yalou Huang
Venue: CIKM
Year: 2011

The ever increasing usage of acronyms in many kinds of documents, including web pages, is becoming an obstacle for average readers. This paper studies the task of finding expansions in documents for a given set of acronyms. We cast the expansion finding problem as a sequence labeling task and adapt Conditional Random Fields (CRF) to solve it. While adapting CRFs, we enhance the performance from two aspects. First, we introduce nonlinear hidden layers to learn better representations of the input data. Second, we design simple and effective features. We create a hand labeled evaluation data based on Wikipedia.org and web crawling. We evaluate the effectiveness of several algorithms in solving the expansion finding problem. The experimental results demonstrate that the new method achieves performs better than Support Vector Machine and standard Conditional Random Fields.
User browsing behavior-driven web crawling
Authors: Minghai Liu, Rui Cai, Ming Zhang, Lei Zhang
Venue: CIKM
Year: 2011

To optimize the performance of web crawlers, various page importance measures have been studied to select and order URLs in crawling. Most sophisticated measures (e.g. breadth-first and PageRank) are based on link structure. In this paper, we treat the problem from another perspective and propose to measure page importance through mining user interest and behaviors from web browse logs. Unlike most existing approaches which work on single URL, in this paper, both the log mining and the crawl ordering are performed at the granularity of URL pattern. The proposed URL pattern-based crawl orderings are capable to properly predict the importance of newly created (unseen) URLs. Promising experimental results proved the feasibility of our approach.
Finding relevant information of certain types from enterprise data
Authors: Xitong Liu, Hui Fang, Conglei Yao, Min Wang
Venue: CIKM
Year: 2011

Search over enterprise data is essential to every aspect of an enterprise because it helps users fulfill their information needs. Similar to Web search, most queries in enterprise search are keyword queries. However, enterprise search is a unique research problem because, compared with the data in traditional IR applications (e.g., text data), enterprise data includes information stored in different formats. In particular, enterprise data include both unstructured and structured information, and all the data center around a particular enterprise. As a result, the relevant information from these two data sources could be complementary to each other. Intuitively, such integrated data could be exploited to improve the enterprise search quality. Despite its importance, this problem has received little attention so far. In this paper, we demonstrate the feasibility of leveraging the integrated information in enterprise data to improve search quality through a case study, i.e., finding relevant information of certain types from enterprise data. Enterprise search users often look for different types of relevant information other than documents, e.g., the contact information of per- sons working on a product. When formulating a keyword query, search users may specify both content requirements, i.e., what kind of information is relevant, and type requirements, i.e., what type of information is relevant. Thus, the goal is to find information relevant to both requirements specified in the query. Specifically, we formulate the problem as keyword search over structured or semistructured data, and then propose to leverage the complementary unstructured information in the enterprise data to solve the problem. Experiment results over real world enterprise data and simulated data show that the proposed methods can effectively exploit the unstructured information to find relevant information of certain types from structured and semistructured information in enterprise data.
Sentiment classification via l2-norm deep belief network
Authors: Tao Liu, Minghui Li, Shusen Zhou, Xiaoyong Du
Venue: CIKM
Year: 2011

Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets.
Unsupervised transactional query classification based on webpage form understanding
Authors: Yuchen Liu, Xiaochuan Ni, Jian-Tao Sun, Zheng Chen
Venue: CIKM
Year: 2011

Query type classification aims to classify search queries into categories like navigational, informational and transactional, etc., according to the type of information need behind the queries. Although this problem has drawn many research attentions, previous methods usually require editors to label queries as training data or need domain knowledge to edit rules for predicting query type. Also, the existing work has been mainly focusing on the classification of informational and navigational query types. Transactional query classification has not been well addressed. In this work, we propose an unsupervised approach for transactional query classification. This method is based on the observation that, after the transactional queries are issued to a search engine, many users will click the search result pages and then have interactions with Web forms on these pages. The interactions, e.g., typing in text box, making selections from dropdown list, clicking on a button to execute actions, are used to specify detailed information of the transaction. By mining toolbar search log data, which records the associations between queries and Web forms clicked by users, we can get a set of good quality transactional queries without using manual labeling efforts. By matching these automatically acquired transactional queries and their associated Web form contents, we can generalize these queries into patterns. These patterns can be used to classify queries which are not covered by search log. Our experiments indicate that transactional queries produced by this method have good quality. The pattern based classifier achieves 83% F1 classification result. This is very effective considering the fact that we do not adopt any labeling efforts to train the classifier.
Large-scale behavioral targeting with a social twist
Authors: Kun Liu, Lei Tang
Venue: CIKM
Year: 2011

Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.
Feature selection using hierarchical feature clustering
Authors: Huawen Liu, Xindong Wu, Shichao Zhang
Venue: CIKM
Year: 2011

One of the challenges in data mining is the dimensionality of data, which is often very high and prevalent in many domains, such as text categorization and bio-informatics. The high-dimensionality of data may bring many adverse situations to traditional learning algorithms. To cope with this issue, feature selection has been put forward. Currently, many efforts have been attempted in this field and lots of feature selection algorithms have been developed. In this paper we propose a new selection method to pick discriminative features by using information measurement. The main characteristic of our selection method is that the selection procedure works like feature clustering in a hierarchically agglomerative way, where each feature is considered as a cluster and the between-cluster and within-cluster distances are measured by mutual information and the coefficient of relevancy respectively. Consequently, the final aggregated cluster is the selection result, which has the minimal redundancy among its members and the maximal relevancy with the class labels. The simulation experiments on seven datasets show that the proposed method outperforms other popular feature selection algorithms in classification performance.
Subject-oriented top-k hot region queries in spatial dataset
Authors: Junling Liu, Ge Yu, Huanliang Sun
Venue: CIKM
Year: 2011

This paper proposes and solves a novel type of spatial queries named Subject-oriented Top-k hot Region (STR) queries. Given a subject S defined by a feature set R and features importance denoted by weights, an STR query retrieves k non-overlapping regions that have the highest scores computed by the number of feature objects and their weights. As an example, the culture subject is defined by exhibition halls, libraries and museums. On the subject, an STR query finds cultural centers intensively distributed feature objects. In this paper, we propose two efficient algorithms, single-partition (SP) algorithm and dual-partition (DP) algorithm, to process STR queries. Extensive experiments evaluate the proposed solutions under a wide range of parameter settings.
Black swan: augmenting statistics with event data
Authors: Johannes Lorey, Felix Naumann, Benedikt Forchhammer, Andrina Mascher, Peter Retzlaff, Armin Zamani Farahani, Soeren Discher, Cindy Faehnrich, Stefan Lemme, Thorsten Papenbrock, Robert Christoph Peschel, Stephan Richter, Thomas Stening, Sven Viehmeier
Venue: CIKM
Year: 2011

A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively.
Legal document clustering with built-in topic segmentation
Authors: Qiang Lu, Jack G. Conrad, Khalid Al-Kofahi, William Keenan
Venue: CIKM
Year: 2011

Clustering is a useful tool for helping users navigate, summarize, and organize large quantities of textual documents available on the Internet, in news sources, and in digital libraries. A variety of clustering methods have also been applied to the legal domain, with various degrees of success. Some unique characteristics of legal content as well as the nature of the legal domain present a number of challenges. For example, legal documents are often multi-topical, contain carefully crafted, professional, domain-specific language, and possess a broad and unevenly distributed coverage of legal issues. Moreover, unlike widely accessible documents on the Internet, where search and categorization services are generally free, the legal profession is still largely a fee-for-service field that makes the quality (e.g., in terms of both recall and precision) a key differentiator of provided services. This paper introduces a classification-based recursive soft clustering algorithm with built-in topic segmentation. The algorithm leverages existing legal document metadata such as topical classifications, document citations, and click stream data from user behavior databases, into a comprehensive clustering framework. Techniques associated with the algorithm have been applied successfully to very large databases of legal documents, which include judicial opinions, statutes, regulations, administrative materials and analytical documents. Extensive evaluations were conducted to determine the efficiency and effectiveness of the proposed algorithm. Subsequent evaluations conducted by legal domain experts have demonstrated that the quality of the resulting clusters based upon this algorithm is similar to those created by domain experts.
Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
Authors: Meizhu Liu, Le Lu, Xiaojing Ye, Shipeng Yu, Heng Huang
Venue: CIKM
Year: 2011

Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivity with desirably low false positive (FP) rate is critical for a CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. Given various spurious imagery noises which cause observation uncertainties, this remains a very challenging task. In this paper, we propose a novel, two-tiered coarse-to-fine (CTF) classification cascade framework to tackle this problem. We first obtain classification-critical data samples (e.g., implicit samples on the decision boundary) extracted from the holistic data distributions using a robust parametric model (e.g., [13]); then we build a graph-embedding based nonparametric classifier on sampled data, which can more accurately preserve or formulate the complex classification boundary. These two steps can also be considered as effective "sample pruning" and "feature pursuing + kNN/template matching", respectively. Our approach is validated comprehensively in colorectal polyp detection and lung nodule detection CAD systems, as the top two deadly cancers, using hospital scale, multi-site clinical datasets. The results show that our method achieves overall better classification/detection performance than existing state-of-the-art algorithms using single-layer classifiers, such as the support vector machine variants [17], boosting [15], logistic regression [11], relevance vector machine [13], k-nearest neighbor [9] or spectral projections on graph [2].
LSDS-IR'11: the 9th workshop on large-scale and distributed systems for information retrieval
Authors: Claudio Lucchese, Berkant Barla Cambazoglu
Venue: CIKM
Year: 2011

The growth of the Web and user bases lead to important performance problems for large-scale Web search engines. The LSDS- IR '11 workshop focuses on research contributions related to the scalability and efficiency of distributed information retrieval (IR) systems. The workshop also encourages contributions that propose different ways of leveraging diversity and multiplicity of resources available in distributed systems. More specifically, we are interested in novel applications, models, and architectures that deal with efficiency and scalability of distributed IR systems.
4th international workshop on patent information retrieval (PaIR'11)
Authors: Mihai Lupu, Allan Hanbury, Andreas Rauber
Venue: CIKM
Year: 2011

The 4th International Workshop on Patent Information Retrieval builds on the experiences of the first three workshops, to provide its participants an exciting, scientifically challenging and interactive event, where specific issues of patent retrieval may be put into the general context of Information Retrieval and Knowledge Management, in order to explore innovative solutions to new and old problems, but also to evaluate and adapt traditional or classic approaches to new problems. This year, we observe an increase in the use of standardized test collections in the contributions received, and, at the same time, new discussion points on how to make such standardized evaluation exercises more accessible to the larger IP community.
Adaptive term frequency normalization for BM25
Authors: Yuanhua Lv, ChengXiang Zhai
Venue: CIKM
Year: 2011

A key component of BM25 contributing to its success is its sub linear term frequency (TF) normalization formula. The scale and shape of this TF normalization component is controlled by a parameter k1, which is generally set to a term-independent constant. We hypothesize and show empirically that in order to optimize retrieval performance, this parameter should be set in a term-specific way. Following this intuition, we propose an information gain measure to directly estimate the contributions of repeated term occurrences, which is then exploited to fit the BM25 function to predict a term-specific k1. Our experiment results show that the proposed approach, without needing any training data, can efficiently and automatically estimate a term-specific k1, and is more effective and robust than the standard BM25.