OpenRaiser/Intern-Atlas huggingface dataset:
Entropy descriptor for image classification
Authors: Hongyu Li, Junyu Niu, Jiachen Chen, Huibo Liu
Venue: SIGIR
Year: 2010

This paper presents a novel entropy descriptor in the sense of geometric manifolds. With this descriptor, entropy cycles can be easily designed for image classification. Minimizing this entropy leads to an optimal entropy cycle where images are connected in the semantic order. During classification, the training step is to find an optimal entropy cycle in each class. In the test step, an unknown image is grouped into a class if the entropy increase as the result of inserting the image into the cycle of this class is relatively least. The proposed approach can generalize well on difficult image classification problems where images with same objects are taken in multiple views. Experimental results show that this entropy descriptor performs well in image classification and has potential in the image-based modeling retrieval.
Ontology-enriched multi-document summarization in disaster management
Authors: Lei Li, Dingding Wang, Chao Shen, Tao Li
Venue: SIGIR
Year: 2010

In this poster, we propose a novel document summarization approach named Ontology-enriched Multi-Document Summarization(OMS) for utilizing background knowledge to improve summarization results. OMS first maps the sentences of input documents onto an ontology, then links the given query to a specific node in the ontology, and finally extracts the summary from the sentences in the subtree rooted at the query node. By using the domain-related ontology, OMS can better capture the semantic relevance between the query and the sentences, and thus lead to better summarization results. As a byproduct, the final summary generated by OMS can be represented as a tree showing the hierarchical relationships of the extracted sentences. Evaluation results on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy of OMS.
Exploring desktop resources based on user activity analysis
Authors: Yukun Li, Xiangyu Zhang, Xiaofeng Meng
Venue: SIGIR
Year: 2010

Relocation in personal desktop resources is an interesting and promising research topic. This demonstration illustrates a new perspective in exploring desktop resources to help users re-find expected data resources more effectively. Different from existing works, our prototype OrientSpace has two features: automatically extract and maintain user tasks to support task-based exploration, and support vague search by exploiting associations between desktop resources.
Estimating probabilities for effective data fusion
Authors: David Lillis, Lusheng Zhang, Fergus Toolan, Rem W. Collier, David Leonard, John Dunnion
Venue: SIGIR
Year: 2010

Data Fusion is the combination of a number of independent search results, relating to the same document collection, into a single result to be presented to the user. A number of probabilistic data fusion models have been shown to be effective in empirical studies. These typically attempt to estimate the probability that particular documents will be relevant, based on training data. However, little attempt has been made to gauge how the accuracy of these estimations affect fusion performance. The focus of this paper is twofold: firstly, that accurate estimation of the probability of relevance results in effective data fusion; and secondly, that an effective approximation of this probability can be made based on less training data that has previously been employed. This is based on the observation that the distribution of relevant documents follows a similar pattern in most high-quality result sets. Curve fitting suggests that this can be modelled by a simple function that is less complex than other models that have been proposed. The use of existing IR evaluation metrics is proposed as a substitution for probability calculations. Mean Average Precision is used to demonstrate the effectiveness of this approach, with evaluation results demonstrating competitive performance when compared with related algorithms with more onerous requirements for training data.
Learning to rank for information retrieval
Authors: Tie-Yan Liu
Venue: SIGIR
Year: 2010

This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.
Personalizing information retrieval for multi-session tasks: the roles of task stage and task type
Authors: Jingjing Liu, Nicholas J. Belkin
Venue: SIGIR
Year: 2010

Dwell time as a user behavior has been found in previous studies to be an unreliable predictor of document usefulness, with contextual factors such as the user's task needing to be considered in its interpretation. Task stage has been shown to influence search behaviors including usefulness judgments, as has task type. This paper reports on an investigation of how task stage and task type may help predict usefulness from the time that users spend on retrieved documents, over the course of several information seeking episodes. A 3-stage controlled experiment was conducted with 24 participants, each coming 3 times to work on 3 sub-tasks of a general task, couched either as "parallel" or "dependent" task type. The full task was to write a report on the general topic, with interim documents produced for each sub-task. Results show that task stage can help in inferring document usefulness from decision time, especially in the parallel task. The findings can be used to increase accuracy in predicting document usefulness and accordingly in personalizing search for multi-session tasks.
Can search systems detect users' task difficulty?: some behavioral signals
Authors: Jingjing Liu, Chang Liu, Jacek Gwizdka, Nicholas J. Belkin
Venue: SIGIR
Year: 2010

In this paper, we report findings on how user behaviors vary in tasks with different difficulty levels as well as of different types. Two behavioral signals: document dwell time and number of content pages viewed per query, were found to be able to help the system detect when users are working with difficult tasks.
Multi-field learning for email spam filtering
Authors: Wuying Liu, Ting Wang
Venue: SIGIR
Year: 2010

Through the investigation of email document structure, this paper proposes a multi-field learning (MFL) framework, which breaks the multi-field document Text Classification (TC) problem into several sub-document TC problems, and makes the final category prediction by weighted linear combination of several sub-document TC results. Many previous statistical TC algorithms can be easily rebuilt within the MFL framework via turning binary result to spamminess score, which is a real number and reflects the likelihood that the classified email is spam. The experimental results in the TREC spam track show that the performances of many TC algorithms can be improved within the MFL framework.
S-PLASA+: adaptive sentiment analysis with application to sales performance prediction
Authors: Yang Liu, Xiaohui Yu, Xiangji Huang, Aijun An
Venue: SIGIR
Year: 2010

Analyzing the large volume of online reviews would produce useful knowledge that could be of economic values to vendors and other interested parties. In particular, the sentiments expressed in the online reviews have been shown to be strongly correlated with the sales performance of products. In this paper, we present an adaptive sentiment analysis model called S-PLSA+, which aims to capture the hidden sentiment factors in the reviews with the capability to be incrementally updated as more data become available. We show how S-PLSA+ can be applied to sales performance prediction using an ARSA model developed in previous literature. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.
Learning to rank audience for behavioral targeting
Authors: Ning Liu, Jun Yan, Dou Shen, Depin Chen, Zheng Chen, Ying Li
Venue: SIGIR
Year: 2010

Behavioral Targeting (BT) is a recent trend of online advertising market. However, some classical BT solutions, which predefine the user segments for BT ads delivery, are sometimes too large to numerous long-tail advertisers, who cannot afford to buy any large user segments due to budget consideration. In this extend abstract, we propose to rank users according to their probability of interest in an advertisement in a learning to rank framework. We propose to extract three types of features between user behaviors such as search queries, ad click history etc and the ad content provided by advertisers. Through this way, a long-tail advertiser can select a certain number of top ranked users as needed from the user segments for ads delivery. In the experiments, we use a 30-days' ad click-through log from a commercial search engine. The results show that using our proposed features under a learning to rank framework, we can well rank users who potentially interest in an advertisement.
Active learning for ranking through expected loss optimization
Authors: Bo Long, Olivier Chapelle, Ya Zhang, Yi Chang, Zhaohui Zheng, Belle L. Tseng
Venue: SIGIR
Year: 2010

Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking. In this paper, we propose a general active learning framework, Expected Loss Optimization (ELO), for ranking. The ELO framework is applicable to a wide range of ranking functions. Under this framework, we derive a novel algorithm, Expected DCG Loss Optimization (ELO-DCG), to select most informative examples. Furthermore, we investigate both query and document level active learning for raking and propose a two-stage ELO-DCG algorithm which incorporate both query and document selection into active learning. Extensive experiments on real-world Web search data sets have demonstrated great potential and effective-ness of the proposed framework and algorithms.
Using local precision to compare search engines in consumer health information retrieval
Authors: Carla Teixeira Lopes, Cristina Ribeiro
Venue: SIGIR
Year: 2010

We have conducted a user study to evaluate several generalist and health-specific search engines on health information retrieval. Users evaluated the relevance of the top 30 documents of 4 search engines in two different health information needs. We introduce the concepts of local and global precision and analyze how they affect the evaluation. Results show that Google surpasses the precision of all other engines, including the health-specific ones, and that precision differs with the type of clinical question and its medical specialty.
Personalize web search results with user's location
Authors: Yumao Lu, Fuchun Peng, Xing Wei, Benoît Dumoulin
Venue: SIGIR
Year: 2010

We build a probabilistic model to identify implicit local intent queries, and leverage user's physical location to improve Web search results for these queries. Evaluation on commercial search engine shows significant improvement on search relevance and user experience.
PRES: a score metric for evaluating recall-oriented information retrieval applications
Authors: Walid Magdy, Gareth J. F. Jones
Venue: SIGIR
Year: 2010

Information retrieval (IR) evaluation scores are generally designed to measure the effectiveness with which relevant documents are identified and retrieved. Many scores have been proposed for this purpose over the years. These have primarily focused on aspects of precision and recall, and while these are often discussed with equal importance, in practice most attention has been given to precision focused metrics. Even for recall-oriented IR tasks of growing importance, such as patent retrieval, these precision based scores remain the primary evaluation measures. Our study examines different evaluation measures for a recall-oriented patent retrieval task and demonstrates the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and the user's search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the user's expected search effort.
Leveraging user-generated content for news search
Authors: Richard McCreadie
Venue: SIGIR
Year: 2010

Over the last few years both availability and accessibility of current news stories on the Web have dramatically improved. In particular, users can now access news from a variety of sources hosted on the Web, from newswire presences such as the New York Times, to integrated news search within Web search engines. However, of central interest is the emerging impact that user-generated content (UGC) is having on this online news landscape. Indeed, the emergence of Web 2.0 has turned a static news consumer base into a dynamic news machine, where news stories are summarised and commented upon. In summary, value is being added to each news story in terms of additional content.
Incorporating global information into named entity recognition systems using relational context
Authors: Yuval Merhav, Filipe de Sá Mesquita, Denilson Barbosa, Wai Gen Yee, Ophir Frieder
Venue: SIGIR
Year: 2010

The state-of-the-art in Named Entity Recognition relies on a combination of local features of the text and global knowledge to determine the types of the recognized entities. This is problematic in some cases, resulting in entities being classified as belonging to the wrong type. We show that using global information about the corpus improves the accuracy of type identification. We explore the notion of a global domain frequency that relates relation identifying terms with pairs of entity types which are used in that relation. We use this to identify entities whose types are not compatible with the terms they co-occur in the text. Our results on a large corpus of social media content allows the identification of mistyped entities with 70% accuracy.
Semi-supervised spam filtering using aggressive consistency learning
Authors: Mona Mojdeh, Gordon V. Cormack
Venue: SIGIR
Year: 2010

A graph based semi-supervised method for email spam filtering, based on the local and global consistency method, yields low error rates with very few labeled examples. The motivating application of this method is spam filters with access to very few labeled message. For example, during the initial deployment of a spam filter, only a handful of labeled examples are available but unlabeled examples are plentiful. We demonstrate the performance of our approach on TREC 2007 and CEAS 2008 email corpora. Our results compare favorably with the best-known methods, using as few as just two labeled examples: one spam and one non-spam.
Hierarchical pitman-yor language model for information retrieval
Authors: Saeedeh Momtazi, Dietrich Klakow
Venue: SIGIR
Year: 2010

In this paper, we propose a new application of Bayesian language model based on Pitman-Yor process for information retrieval. This model is a generalization of the Dirichlet distribution. The Pitman-Yor process creates a power-law distribution which is one of the statistical properties of word frequency in natural language. Our experiments on Robust04 indicate that this model improves the document retrieval performance compared to the commonly used Dirichlet prior and absolute discounting smoothing techniques.
User interface designs to support the social transfer of web search expertise
Authors: Neema Moraveji
Venue: SIGIR
Year: 2010

While there are many ways to develop search expertise, I maintain that most members of the general public do so in an inefficient manner. One reason is that, with current tools, is difficult to observe experts as a means of acquiring search expertise in a scalable fashion. This calls for a redesign of computer-mediated communication tools to make individual search strategies visible to other users. I present a research agenda to investigate this claim, which draws upon theories of social learning. I use design-based research to build novel systems that enable imitation-based learning of search expertise.
Analysis of structural relationships for hierarchical cluster labeling
Authors: Markus Muhr, Roman Kern, Michael Granitzer
Venue: SIGIR
Year: 2010

Cluster label quality is crucial for browsing topic hierarchies obtained via document clustering. Intuitively, the hierarchical structure should influence the labeling accuracy. However, most labeling algorithms ignore such structural properties and therefore, the impact of hierarchical structures on the labeling accuracy is yet unclear. In our work we integrate hierarchical information, i.e. sibling and parent-child relations, in the cluster labeling process. We adapt standard labeling approaches, namely Maximum Term Frequency, Jensen-Shannon Divergence, Chi Square Test, and Information Gain, to take use of those relationships and evaluate their impact on 4 different datasets, namely the Open Directory Project, Wikipedia, TREC Ohsumed and the CLEF IP European Patent dataset. We show, that hierarchical relationships can be exploited to increase labeling accuracy especially on high-level nodes.
A network-based model for high-dimensional information filtering
Authors: Nikolaos Nanas, Manolis Vavalis, Anne N. De Roeck
Venue: SIGIR
Year: 2010

The Vector Space Model has been and to a great extent still is the de facto choice for profile representation in content-based Information Filtering. However, user profiles represented as weighted keyword vectors have inherent dimensionality problems. As the number of profile keywords increases, the vector representation becomes ambiguous, due to the exponential increase in the volume of the vector space and in the number of possible keyword combinations. We argue that the complexity and dynamics of Information Filtering require user profile representations which are resilient and resistant to this "curse of dimensionality". A user profile has to be able to incorporate many features and to adapt to a variety of interest changes. We propose an alternative, network-based profile representation that meets these challenging requirements. Experiments show that the network profile representation can more effectively capture additional information about a user's interests and thus achieve significant performance improvements over a vector-based representation comprising the same weighted keywords.
The value of visual elements in web search
Authors: Marilyn Ostergren, Seung-yon Yu, Efthimis N. Efthimiadis
Venue: SIGIR
Year: 2010

We used eye-tracking equipment to observe 36 participants as they performed three search tasks using three graphically-enhanced web search interfaces (Kartoo, SearchMe and Viewzi). In this poster we describe findings of the study focusing on how the presentation of SERP results influences how the user scans and attends to the results, and the user satisfaction with these search engines.
Estimating advertisability of tail queries for sponsored search
Authors: Sandeep Pandey, Kunal Punera, Marcus Fontoura, Vanja Josifovski
Venue: SIGIR
Year: 2010

Sponsored search is one of the major sources of revenue for search engines on the World Wide Web. It has been observed that while showing ads for every query maximizes short-term revenue, irrelevant ads lead to poor user experience and less revenue in the long-term. Hence, it is in search engines' interest to place ads only for queries that are likely to attract ad-clicks. Many algorithms for estimating query advertisability exist in literature, but most of these methods have been proposed for and tested on the frequent or "head" queries. Since query frequencies on search engine are known to be distributed as a power-law, this leaves a huge fraction of the queries uncovered.
Blog snippets: a comments-biased approach
Authors: Javier Parapar, Jorge López-Castro, Alvaro Barreiro
Venue: SIGIR
Year: 2010

In the last years Blog Search has been a new exciting task in Information Retrieval. The presence of user generated information with valuable opinions makes this field of huge interest. In this poster we use part of this information, the readers' comments, to improve the quality of post snippets with the objective of enhancing the user access to the relevant posts in a result list. We propose a simple method for snippet generation based on sentence selection, using the comments to guide the selection process. We evaluated our approach with standard TREC methodology in the Blogs06 collection showing significant improvements up to 32% in terms of MAP over the baseline.
Extraction of open-domain class attributes from text: building blocks for faceted search
Authors: Marius Pasca
Venue: SIGIR
Year: 2010

Knowledge automatically extracted from text captures instances, classes of instances and relations among them. In particular, the acquisition of class attributes (e.g., "top speed", "body style" and "number of cylinders" for the class of "sports cars") from text is a particularly appealing task and has received much attention recently, given its natural fit as a building block towards the far-reaching goal of constructing knowledge bases from text. This tutorial provides an overview of extraction methods developed in the area of Web-based information extraction, with the purpose of acquiring attributes of open-domain classes. The attributes are extracted for classes organized either as a flat set or hierarchically. The extraction methods operate over unstructured or semi-structured text available within collections of Web documents, or over relatively more intriguing data sources consisting of anonymized search queries. The methods take advantage of weak supervision provided in the form of seed examples or small amounts of annotated data, or draw upon knowledge already encoded within human-compiled resources (e.g., Wikipedia). The more ambitious methods, aiming at acquiring as many accurate attributes from text as possible for hundreds or thousands of classes covering a wide range of domains of interest, need to be designed to scale to Web collections. This restriction has significant consequences on the overall complexity and choice of underlying tools, in order for the extracted attributes to ultimately aid information retrieval in general and Web search in particular, by producing relevant attributes for open-domain classes, along with other types of relations among instances or among classes.