OpenRaiser/Intern-Atlas huggingface dataset:
Spatial relationships in visual graph modeling for image categorization
Authors: Trong-Ton Pham, Philippe Mulhem, Loïc Maisonnasse
Venue: SIGIR
Year: 2010

In this paper, a language model adapted to graph-based representation of image content is proposed and assessed. The full indexing and retrieval processes are evaluated on two different image corpora. We show that using the spatial relationships with graph model has a positive impact on the results of standard Language Model (LM) and outperforms the baseline built upon the current state-of-the-art Support Vector Machine (SVM) classification method.
Automatic content linking: speech-based just-in-time retrieval for multimedia archives
Authors: Andrei Popescu-Belis, Jonathan Kilgour, Peter Poller, Alexandre Nanchen, Erik M. Boertjes, Joost de Wit
Venue: SIGIR
Year: 2010

The Automatic Content Linking Device monitors a conversation and uses automatically recognized words to retrieve documents that are of potential use to the participants. The document set includes project related reports or emails, transcribed snippets of past meetings, and websites. Retrieval results are displayed at regular intervals.
Re-examination on lam% in spam filtering
Authors: Haoliang Qi, Muyun Yang, Xiaoning He, Sheng Li
Venue: SIGIR
Year: 2010

Logistic average misclassification percentage (lam%) is a key measure for the spam filtering performance. This paper demonstrates that a spam filter can achieve a perfect 0.00% in lam%, the minimal value in theory, by simply setting a biased threshold during the classifier modeling. At the same time, the overall classification performance reaches only a low accuracy. The result suggests that the role of lam% for spam filtering evaluation should be re-examined.
Metrics for assessing sets of subtopics
Authors: Filip Radlinski, Martin Szummer, Nick Craswell
Venue: SIGIR
Year: 2010

To evaluate the diversity of search results, test collections have been developed that identify multiple intents for each query. Intents are the different meanings or facets that should be covered in a search results list. This means that topic development involves proposing a set of intents for each query. We propose four measurable properties of query-to-intent mappings, allowing for more principled topic development for such test collections.
Suggesting related topics in web search
Authors: Santosh Raju, Shaishav Kumar, Raghavendra Udupa
Venue: SIGIR
Year: 2010

Suggesting topics that are related to user's goal or interest is very important in web search. However, search engines today focus on suggesting mainly reformulations and lexical variants of the query mined from query logs. In this demonstration, we show a system that can suggest related topics for a query based on the top search results for the query. It can help users in exploring the topics related to their information need. The topic suggestion system can be integrated with any search engine or it can be easily installed on the client machine as a browser plugin.
Cross-language retrieval using link-based language models
Authors: Benjamin Roth, Dietrich Klakow
Venue: SIGIR
Year: 2010

We propose a cross-language retrieval model that is solely based on Wikipedia as a training corpus. The main contributions of our work are: 1. A translation model based on linked text in Wikipedia and a term weighting method associated with it. 2. A combination scheme to interpolate the link translation model with retrieval based on Latent Dirichlet Allocation. On the CLEF 2000 data we achieve improvement with respect to the best German-English system at the bilingual track (non-significant) and improvement against a baseline based on machine translation (significant).
Aspect presence verification conditional on other aspects
Authors: Dmitri Roussinov
Venue: SIGIR
Year: 2010

I have shown that the presence of difficult query aspects that are revealed only implicitly (e.g. exploration, opposition, achievements, cooperation, risks) can be improved by taking advantage of the known presence of other, easier to verify query aspects. The approach proceeds by mining a large external corpus and results in substantial improvements in re-ranking the subset of the top retrieved documents.
Visual concept-based selection of query expansions for spoken content retrieval
Authors: Stevan Rudinac, Martha A. Larson, Alan Hanjalic
Venue: SIGIR
Year: 2010

In this paper we present a novel approach to semantic-theme-based video retrieval that considers entire videos as retrieval units and exploits automatically detected visual concepts to improve the results of retrieval based on spoken content. We deploy a query prediction method that makes use of a coherence indicator calculated on top returned documents and taking into account the information about visual concepts presence in videos to make a choice between query expansion methods. The main contribution of our approach is in its ability to exploit noisy shot-level concept detection to improve semantic-theme-based video retrieval. Strikingly, improvement is possible using an extremely limited set of concepts. In the experiments performed on TRECVID 2007 and 2008 datasets our approach shows an interesting performance improvement compared to the best performing baseline.
Multimedia information retrieval
Authors: Stefan M. Rüger
Venue: SIGIR
Year: 2010

This tutorial is concerned with creating the best possible multimedia search experience. The intriguing bit here is that the query itself can be a multimedia excerpt: For example, when you walk around in an unknown place and stumble across an interesting landmark, would it not be great if you could just take a picture with your mobile phone and send it to a service that finds a similar picture in a database and tells you more about the building - and about its significance for that matter?
Temporally-aware algorithms for document classification
Authors: Thiago Salles, Leonardo Rocha, Gisele L. Pappa, Fernando Mourão, Wagner Meira Jr., Marcos André Gonçalves
Venue: SIGIR
Year: 2010

Automatic Document Classification (ADC) is still one of the major information retrieval problems. It usually employs a supervised learning strategy, where we first build a classification model using pre-classified documents and then use this model to classify unseen documents. The majority of supervised algorithms consider that all documents provide equally important information. However, in practice, a document may be considered more or less important to build the classification model according to several factors, such as its timeliness, the venue where it was published in, its authors, among others. In this paper, we are particularly concerned with the impact that temporal effects may have on ADC and how to minimize such impact. In order to deal with these effects, we introduce a temporal weighting function (TWF) and propose a methodology to determine it for document collections. We applied the proposed methodology to ACM-DL and Medline and found that the TWF of both follows a lognormal. We then extend three ADC algorithms (namely kNN, Rocchio and Naïve Bayes) to incorporate the TWF. Experiments showed that the temporally-aware classifiers achieved significant gains, outperforming (or at least matching) state-of-the-art algorithms.
Studying trailfinding algorithms for enhanced web search
Authors: Adish Singla, Ryen White, Jeff Huang
Venue: SIGIR
Year: 2010

Search engines return ranked lists of Web pages in response to queries. These pages are starting points for post-query navigation, but may be insufficient for search tasks involving multiple steps. Search trails mined from toolbar logs start with a query and contain pages visited by one user during post-query navigation. Implicit endorsements from many trails can enhance result ranking. Rather than using trails solely to improve ranking, it may also be worth providing trail information directly to users. In this paper, we quantify the benefit that users currently obtain from trail-following and compare different methods for finding the best trail for a given query and each top-ranked result. We compare the relevance, topic coverage, topic diversity, and utility of trails selected using different methods, and break out findings by factors such as query type and origin relevance. Our findings demonstrate value in trails, highlight interesting differences in the performance of trailfinding algorithms, and show we can find best-trails for a query that outperform the trails most users follow. Findings have implications for enhancing Web information seeking using trails.
Human performance and retrieval precision revisited
Authors: Mark D. Smucker, Chandra Prakash Jethani
Venue: SIGIR
Year: 2010

Several studies have found that the Cranfield approach to evaluation can report significant performance differences between retrieval systems for which little to no performance difference is found for humans completing tasks with these systems. We revisit the relationship between precision and performance by measuring human performance on tightly controlled search tasks and with user interfaces offering limited interaction. We find that human performance and retrieval precision are strongly related. We also find that users change their relevance judging behavior based on the precision of the results. This change in behavior coupled with the well-known lack of perfect inter-assessor agreement can reduce the measured performance gains predicted by increased precision.
Leveraging user interaction and collaboration for improving multilingual information access in digital libraries
Authors: Juliane Stiller
Venue: SIGIR
Year: 2010

The goal of interactive cross-lingual information retrieval systems is to support users in formulating effective queries and selecting the documents which satisfy their information needs regardless of the language of these documents. This dissertation aims at harnessing user-system interaction, extracting the added value and integrating it back into the system to improve cross-lingual information retrieval for successive users. To achieve this, user input at different interaction points will be evaluated. This will, among others, include interaction during user-assisted query translations, implicit and explicit relevance feedback and social tags. To leverage this input, explorative studies need to be conducted to determine beneficial user input and the methods of extracting it.
User centered story tracking
Authors: Ilija Subasic
Venue: SIGIR
Year: 2010

Using data collections available on the Internet has for many people became the main medium for staying informed about the world. Many of these collections are in nature dynamic, evolving as the subjects they describe change. The goal of different research areas is to identify and highlight these changes to better enable readers to track stories. In this work we restrict ourselves to news collections and investigate "real-life" effectiveness and usability of temporal text mining (TTM) story tracking methods. We propose a new story tracking method and build a tool to support it. Additionally, we investigate the effectiveness and usability of story tracking methods and define a new frameworks for automatic and user oriented evaluation. We built methods and tools which allow for understanding, discovery, and search through user interaction. Although there are many TTM methods developed there is a lack of common evaluation procedure. Therefore, we propose an evaluation framework for measuring how different TTM methods discover novel "facts". Apart from the automatic evaluation we are interested in how can users interact with pattens and learn about the underlying subjects of the story they track. For this purpose we propose a user testing environment that measures speed and accuracy in which users can use story tracking methods to discover predefined sets of ground-truth sentences.
Investigating the suboptimality and instability of pseudo-relevance feedback
Authors: Raghavendra Udupa, Abhijit Bhole
Venue: SIGIR
Year: 2010

Although Pseudo-Relevance Feedback (PRF) techniques improve average retrieval performance at the price of high variance, not much is known about their optimality and the reasons for their instability. In this work, we study more than 800 topics from several test collections including the TREC Robust Track and show that PRF techniques are highly suboptimal, i.e. they do not make the fullest utilization of pseudo-relevant documents and under-perform. A careful selection of expansion terms from the pseudo-relevant document with the help of an oracle can actually improve retrieval performance dramatically (by > 60%). Further, we show that instability in PRF techniques is mainly due to wrong selection of expansion terms from the pseudo-relevant documents. Our findings emphasize the need to revisit the problem of term selection to make a break through in PRF.
EUSUM: extracting easy-to-understand english summaries for non-native readers
Authors: Xiaojun Wan, Huiying Li, Jianguo Xiao
Venue: SIGIR
Year: 2010

In this paper we investigate a novel and important problem in multi-document summarization, i.e., how to extract an easy-to-understand English summary for non-native readers. Existing summarization systems extract the same kind of English summaries from English news documents for both native and non-native readers. However, the non-native readers have different English reading skills because they have different English education and learning backgrounds. An English summary which can be easily understood by native readers may be hardly understood by non-native readers. We propose to add the dimension of reading easiness or difficulty to multi-document summarization, and the proposed EUSUM system can produce easy-to-understand summaries according to the English reading skills of the readers. The sentence-level reading easiness (or difficulty) is predicted by using the SVM regression method. And the reading easiness score of each sentence is then incorporated into the summarization process. Empirical evaluation and user study have been performed and the results demonstrate that the EUSUM system can produce more easy-to-understand summaries for non-native readers than existing summarization systems, with very little sacrifice of the summary's informativeness.
Learning hidden variable models for blog retrieval
Authors: Mengqiu Wang
Venue: SIGIR
Year: 2010

We describe probabilistic models that leverage individual blog post evidence to improve blog seed retrieval performances. Our model offers a intuitive and principled method to combine multiple posts in scoring a whole blog site by treating individual posts as hidden variables. When applied to the seed retrieval task, our model yields state-of-the-art results on the TREC 2007 Blog Distillation Task dataset.
Feature subset non-negative matrix factorization and its applications to document understanding
Authors: Dingding Wang, Chris H. Q. Ding, Tao Li
Venue: SIGIR
Year: 2010

In this paper, we propose feature subset non-negative matrix factorization (NMF), which is an unsupervised approach to simultaneously cluster data points and select important features. We apply our proposed approach to various document understanding tasks including document clustering, summarization, and visualization. Experimental results demonstrate the effectiveness of our approach for these tasks.
Effective query expansion with the resistance distance based term similarity metric
Authors: Shuguang Wang, Milos Hauskrecht
Venue: SIGIR
Year: 2010

In this paper, we define a new query expansion method that relies on term similarity metric derived from the electric resistance network. This proposed metric lets us measure the mutual relevancy in between terms and between their groups. This paper shows how to define this metric automatically from the document collection, and then apply it in query expansion for document retrieval tasks. The experiments show this method can be used to find good expansion terms of search queries and improve document retrieval performance on two TREC genomic track datasets.
Many are better than one: improving multi-document summarization via weighted consensus
Authors: Dingding Wang, Tao Li
Venue: SIGIR
Year: 2010

Given a collection of documents, various multi-document summarization methods have been proposed to generate a short summary. However, few studies have been reported on aggregating different summarization methods to possibly generate better summarization results. We propose a weighted consensus summarization method to combine the results from single summarization systems. Experimental results on DUC2004 data sets demonstrate the performance improvement by aggregating multiple summarization systems, and our proposed weighted consensus summarization method outperforms other combination methods.
Intent boundary detection in search query logs
Authors: Chieh-Jen Wang, Kevin Hsin-Yih Lin, Hsin-Hsi Chen
Venue: SIGIR
Year: 2010

Identifying intent boundary in search query logs is important for learning users' behaviors and applying their experiences. Time-based, query-based, and cluster-based approaches are proposed. Experiments show that the integration of intent clusters and dynamic time model performs the best.
Segmentation of multi-sentence questions: towards effective question retrieval in cQA services
Authors: Kai Wang, Zhaoyan Ming, Xia Hu, Tat-Seng Chua
Venue: SIGIR
Year: 2010

Existing question retrieval models work relatively well in finding similar questions in community-based question answering (cQA) services. However, they are designed for single-sentence queries or bag-of-word representations, and are not sufficient to handle multi-sentence questions complemented with various contexts. Segmenting questions into parts that are topically related could assist the retrieval system to not only better understand the user's different information needs but also fetch the most appropriate fragments of questions and answers in cQA archive that are relevant to user's query. In this paper, we propose a graph based approach to segmenting multi-sentence questions. The results from user studies show that our segmentation model outperforms traditional systems in question segmentation by over 30% in user's satisfaction. We incorporate the segmentation model into existing cQA question retrieval framework for more targeted question matching, and the empirical evaluation results demonstrate that the segmentation boosts the question retrieval performance by up to 12.93% in Mean Average Precision and 11.72% in Top One Precision. Our model comes with a comprehensive question detector equipped with both lexical and syntactic features.
Text document clustering with metric learning
Authors: Jinlong Wang, Shunyao Wu, Huy Quan Vu, Gang Li
Venue: SIGIR
Year: 2010

One reason for semi-supervised clustering fail to deliver satisfactory performance in document clustering is that the transformed optimization problem could have many candidate solutions, but existing methods provide no mechanism to select a suitable one from all those candidates. This paper alleviates this problem by posing the same task as a soft-constrained optimization problem, and introduces the salient degree measure as an information guide to control the searching of an optimal solution. Experimental results show the effectiveness of the proposed method in the improvement of the performance, especially when the amount of priori domain knowledge is limited.
A two-stage model for blog feed search
Authors: Wouter Weerkamp, Krisztian Balog, Maarten de Rijke
Venue: SIGIR
Year: 2010

We consider blog feed search: identifying relevant blogs for a given topic. An individual's search behavior often involves a combination of exploratory behavior triggered by salient features of the information objects being examined plus goal-directed in-depth information seeking behavior. We present a two-stage blog feed search model that directly builds on this insight. We first rank blog posts for a given topic, and use their parent blogs as selection of blogs that we rank using a blog-based model.
Exploring the use of labels to shortcut search trails
Authors: Ryen W. White, Raman Chandrasekar
Venue: SIGIR
Year: 2010

Search trails comprising queries and Web page views are created as searchers engage in information-seeking activity online. During known-item search (where the objective may be to locate a target Web page), searchers may waste valuable time repeatedly reformulating queries as they attempt to locate an elusive page. Trail shortcuts help users bypass unnecessary queries and get them to their desired destination faster. In this poster we present a comparative oracle study of techniques to shortcut sub-optimal search trails using labels derived from social bookmarking, anchor text, query logs, and a human-computation game. We show that labels can help users reach target pages efficiently, that the label sources perform differently, and that shortcuts are potentially most useful when the target is challenging to find.
Assessing the scenic route: measuring the value of search trails in web logs
Authors: Ryen W. White, Jeff Huang
Venue: SIGIR
Year: 2010

Search trails mined from browser or toolbar logs comprise queries and the post-query pages that users visit. Implicit endorsements from many trails can be useful for search result ranking, where the presence of a page on a trail increases its query relevance. Follow-ing a search trail requires user effort, yet little is known about the benefit that users obtain from this activity versus, say, sticking with the clicked search result or jumping directly to the destination page at the end of the trail. In this paper, we present a log-based study estimating the user value of trail following. We compare the relevance, topic coverage, topic diversity, novelty, and utility of full trails over that provided by sub-trails, trail origins (landing pages), and trail destinations (pages where trails end). Our findings demonstrate significant value to users in following trails, especially for certain query types. The findings have implications for the design of search systems, including trail recommendation systems that display trails on search result pages.
Predicting escalations of medical queries based on web page structure and content
Authors: Ryen W. White, Eric Horvitz
Venue: SIGIR
Year: 2010

Logs of users' searches on Web health topics can exhibit signs of escalation of medical concerns, where initial queries about common symptoms are followed by queries about serious, rare illnesses. We present an effort to predict such escalations based on the structure and content of pages encountered during medical search sessions. We construct and then characterize the performance of classifiers that predict whether an escalation will occur after the access of a page. Our findings have implications for ranking algorithms and the design of search interfaces.
Achieving high accuracy retrieval using intra-document term ranking
Authors: Hyun-Wook Woo, Jung-Tae Lee, Seung-Wook Lee, Young-In Song, Hae-Chang Rim
Venue: SIGIR
Year: 2010

Most traditional ranking models roughly score the relevance of a given document by observing simple term statistics, such as the occurrence of query terms within the document or within the collection. Intuitively, the relative importance of query terms with regard to other individual non-query terms in a document can also be exploited to promote the ranks of documents in which the query is dedicated as the main topic. In this paper, we introduce a simple technique named intra-document term ranking, which involves ranking all the terms in a document according to their relative importance within that particular document. We demonstrate that the information regarding the rank positions of given query terms within the intra-document term ranking can be useful for enhancing the precision of top-retrieved results by traditional ranking models. Experiments are conducted on three standard TREC test collections.