OpenRaiser/Intern-Atlas huggingface dataset:
Social networks and discovery in the enterprise (SaND)
Authors: Inbal Ronen, Elad Shahar, Sigalit Ur, Erel Uziel, Sivan Yogev, Naama Zwerdling, David Carmel, Ido Guy, Nadav Har&apos, El, Shila Ofek-Koifman
Venue: SIGIR
Year: 2009

Traditional information discovery methods are based on content: documents, terms, and the relationships between them. In Web 2.0, people come into play as they create documents and tags in many forms. Personalized search, social graphs, content and people recommendation, are some of the tasks that can take advantage of this newly formed ecosystem. The Social Networks and Discovery (SaND) platform is an aggregation tool for information discovery and analysis over social data gathered from Web 2.0 applications in the enterprise. It leverages complex relationships between content and people as surfaced through the social applications to unleash the value of information. Its integrated index supports combining content-based analysis and people-based analysis over a rich data foundation. Enterprise social data is easily modeled and ingested into SaND, and can be further combined with data from external social applications. This demo will present three main functions provided by SaND: Social search: SaND supports search over the social data using a unified approach [1] in which all system entities (documents, people, tags) are searchable and retrievable (See Figure 1). The search UI enables the searcher to get a wider view on the query topic through results from all entity types, while uncovering the relationships between the on-screen entities. Entity recommendation: SaND can be utilized to recommend people and content for the searcher (Figure 2 shows the “Do You Know” widget for people recommendation). People are recommended according to their “social similarity” relations with the searcher, e.g. organizational and friending ties, similar tagging activity and more. Similarly, content that is related to people that are “socially related” to the searcher is recommended as valuable interesting data. Personalization: Search results are personalized by considering the relations of retrieved entities with the searcher. Entities are ranked according to their relevance to the query as well as according to their relationship strength with the searcher.
Serendipitous search via wikipedia: a query log analysis
Authors: Tetsuya Sakai, Kenichi Nogami
Venue: SIGIR
Year: 2009

We analyse the query log of a click-oriented Japanese search engine that utilises the link structures of Wikipedia for encouraging the user to change his information need and to perform repeated, serendipitous, exploratory search. Our results show that users tend to make transitions within the same query type: from person names to person names, from place names to place names, and so on.
Link analysis for private weighted graphs
Authors: Jun Sakuma, Shigenobu Kobayashi
Venue: SIGIR
Year: 2009

Link analysis methods have been used successfully for knowledge discovery from the link structure of mutually linking entities. Existing link analysis methods have been inherently designed based on the fact that the entire link structure of the target graph is observable such as public web documents; however, link information in graphs in the real world, such as human relationship or economic activities, is rarely open to public. If link analysis can be performed using graphs with private links in a privacy-preserving way, it enables us to rank entities connected with private ties, such as people, organizations, or business transactions. In this paper, we present a secure link analysis for graphs with private links by means of cryptographic protocols. Our solutions are designed as privacy-preserving expansions of well-known link analysis methods, PageRank and HITS. The outcomes of our protocols are completely equivalent to those of PageRank and HITS. Furthermore, our protocols theoretically guarantee that the private link information possessed by each node is not revealed to other nodes. %We demonstrate the efficiency of our solution by experimental studies, comparing with existing solutions, such as secure function evaluation, decentralized spectral analysis, and privacy-preserving link-analysis.
Agate: information gathering for risk monitoring
Authors: Arnaud Saval, Yann Mombrun
Venue: SIGIR
Year: 2009

Internet sources provide new ways to acquire information about risk and to follow up the evolution of natural disasters in real time. We will present first, an architecture dedicated to unstructured information processing. Then, we will show how the spatial and temporal representation extended with semantic properties answers information ambiguity problem. Agate platform was evaluated by a non-governmental organization which filtered alerts according to types of disaster and their locations.
A search engine in a few lines.: yes, we can!
Authors: Stefan Savev
Venue: SIGIR
Year: 2009

Many research implementations of search engines are written in C, C++, or Java. They are difficult to understand and modify because they are at least a few thousand lines of code and contain many low-level details. In this paper, we show how to achieve a much shorter and higher level implementation: one in about a few hundred lines. We accomplish this result through the use of a high-level functional programming language, F#, and some of its features such as sequences, pipes and structured input and output. By using a search engine implementation as a case study, we argue that functional programming fits the domain of Information Retrieval problems much better than imperative/OO languages like C++ and Java. Functional programming languages are ideal for rapid algorithm prototyping and data exploration in the field of Information Retrieval (IR). Additionally, our implementation can be used as case study in an IR course since it is a very high level, but nevertheless executable specification of a search engine.
A case for improved evaluation of query difficulty prediction
Authors: Falk Scholer, Steven Garcia
Venue: SIGIR
Year: 2009

Query difficulty prediction aims to identify, in advance, how well an information retrieval system will perform when faced with a particular search request. The current standard evaluation methodology involves calculating a correlation coefficient, to indicate how strongly the predicted query difficulty is related with an actual system performance measure, usually Average Precision. We run a series of experiments based on predictors that have been shown to perform well in the literature, comparing these across different TREC runs. Our results demonstrate that the current evaluation methodology is severely limited. Although it can be used to demonstrate the performance of a predictor for a single system, such performance is not consistent over a variety of retrieval systems. We conclude that published results in the query difficulty area are generally not comparable, and recommend that prediction be evaluated against a spectrum of underlying search systems.
High precision retrieval using relevance-flow graph
Authors: Jangwon Seo, Jiwoon Jeon
Venue: SIGIR
Year: 2009

Traditional bag-of-words information retrieval models use aggregated term statistics to measure the relevance of documents, making it difficult to detect non-relevant documents that contain many query terms by chance or in the wrong context. In-depth document analysis is needed to filter out these deceptive documents. In this paper, we hypothesize that truly relevant documents have relevant sentences in predictable patterns. Our experimental results show that we can successfully identify and exploit these patterns to significantly improve retrieval precision at top ranks.
Integrating phrase inseparability in phrase-based model
Authors: Lixin Shi, Jian-Yun Nie
Venue: SIGIR
Year: 2009

In this paper, we propose a new phrase-based IR model, which integrates a measure of "inseparability" of phrases. Our experiments show its high potential to produce large improvements in retrieval effectiveness.
Effective query expansion for federated search
Authors: Milad Shokouhi, Leif Azzopardi, Paul Thomas
Venue: SIGIR
Year: 2009

&lt;p&gt;While query expansion techniques have been shown to improve retrieval performance in a centralized setting, they have not been well studied in a federated setting. In this paper, we consider how query expansion may be adapted to federated environments and propose several new methods: where focused expansions are used in a selective fashion to produce specific queries for each source (or a set of sources). On a number of different testbeds, we show that focused query expansion can significantly outperform the previously proposed global expansion method, and---contrary to earlier work---show that query expansion can improve performance over standard federated retrieval.&lt;/p&gt;\n\n&lt;p&gt;These findings motivate further research examining the different methods for query expansion, and other forms of system and user interaction, in order to continue improving the performance of interactive federated search systems.&lt;/p&gt;
Agreement among statistical significance tests for information retrieval evaluation at varying sample sizes
Authors: Mark D. Smucker, James Allan, Ben Carterette
Venue: SIGIR
Year: 2009

Research has shown that little practical difference exists between the randomization, Student's paired t, and bootstrap tests of statistical significance for TREC ad-hoc retrieval experiments with 50 topics. We compared these three tests on runs with topic sizes down to 10 topics. We found that these tests show increasing disagreement as the number of topics decreases. At smaller numbers of topics, the randomization test tended to produce smaller p-values than the t-test for p-values less than 0.1. The bootstrap exhibited a systematic bias towards p-values strictly less than the t-test with this bias increasing as the number of topics decreased. We recommend the use of the randomization test although the t-test appears to be suitable even when the number of topics is small.
Experiments in CLIR using fuzzy string search based on surface similarity
Authors: Sethuramalingam Subramaniam, Anil Kumar Singh, Pradeep Dasigi, Vasudeva Varma
Venue: SIGIR
Year: 2009

Cross Language Information Retrieval (CLIR) between languages of the same origin is an interesting topic of research. The similarity of the writing systems used for these languages can be used effectively to not only improve CLIR, but to overcome the problems of textual variations, textual errors, and even the lack of linguistic resources like stemmers to an extent. We have conducted CLIR experiments between three languages which use writing systems (scripts) of Brahmi-origin, namely Hindi, Bengali and Marathi. We found significant improvements for all the six language pairs using a method for fuzzy text search based on Surface Similarity. In this paper we report these results and compare them with a baseline CLIR system and a CLIR system that uses Scaled Edit Distance (SED) for fuzzy string matching.
Robust sparse rank learning for non-smooth ranking measures
Authors: Zhengya Sun, Tao Qin, Qing Tao, Jue Wang
Venue: SIGIR
Year: 2009

Recently increasing attention has been focused on directly optimizing ranking measures and inducing sparsity in learning models. However, few attempts have been made to relate them together in approaching the problem of learning to rank. In this paper, we consider the sparse algorithms to directly optimize the Normalized Discounted Cumulative Gain (NDCG) which is a widely-used ranking measure. We begin by establishing a reduction framework under which we reduce ranking, as measured by NDCG, to the importance weighted pairwise classification. Furthermore, we provide a sound theoretical guarantee for this reduction, bounding the realized NDCG regret in terms of a properly weighted pairwise classification regret, which implies that good performance can be robustly transferred from pairwise classification to ranking. Based on the converted pairwise loss function, it is conceivable to take into account sparsity in ranking models and to come up with a gradient possessing certain performance guarantee. For the sake of achieving sparsity, a novel algorithm named RSRank has also been devised, which performs L1 regularization using truncated gradient descent. Finally, experimental results on benchmark collection confirm the significant advantage of RSRank in comparison with several baseline methods.
Tagging products using image classification
Authors: Brian Tomasik, Phyo Thiha, Douglas Turnbull
Venue: SIGIR
Year: 2009

Associating labels with online products can be a labor-intensive task. We study the extent to which a standard "bag of visual words" image classifier can be used to tag products with useful information, such as whether a sneaker has laces or velcro straps. Using Scale Invariant Feature Transform (SIFT) image descriptors at random keypoints, a hierarchical visual vocabulary, and a variant of nearest-neighbor classification, we achieve accuracies between 66% and 98% on 2- and 3-class classification tasks using several dozen training examples. We also increase accuracy by combining information from multiple views of the same product.
Predicting stopping behaviour: a preliminary analysis
Authors: Elaine G. Toms, Luanne Freund
Venue: SIGIR
Year: 2009

The analysis of search transaction logs often characterizes a search session but rarely looks at the end point. When do users stop, and what cues are present suggesting that stopping is eminent? In this preliminary analysis of the logs of 288 search sessions conducted in a laboratory setting, we identified the activity performed by participants as well as search transitions that were invoked over the course of a search session. The 4331 search transitions (15 per task on average) contained a total of 9295 actions. We isolated the final transition in each search session for detailed analysis. As hypothesized some behaviours are predictable, and suggestive of stopping behavior, with the potential for modeling.
wikiSearch: enabling interactivity in search
Authors: Elaine G. Toms, Tayze Mackenzie, Chris Jordan, Sam Hall
Venue: SIGIR
Year: 2009

wikiSearch, is a search engine customized for the Wikipedia corpus but with design features that may be generalized to other search systems. Its features enhance basic functionality and enable more fluid interactivity while supporting both workflow in the search process and the experimental process used in lab testing.
Visualizing the problems with the INEX topics
Authors: Andrew Trotman, Maria del Rocio Gomez Crisostomo, Mounia Lalmas
Venue: SIGIR
Year: 2009

Topics form a crucial component of a test collection. We show, through visualization, that the INEX 2008 topics have shortcomings, which questions their validity for evaluating XML retrieval effectiveness.
An aspectual interface for supporting complex search tasks
Authors: Robert Villa, Iván Cantador, Hideo Joho, Joemon M. Jose
Venue: SIGIR
Year: 2009

With the increasing importance of search systems on the web, there is a continuing push to design interfaces which are a better match with the kinds of real-world tasks in which users are engaged. In this paper, we consider how broad, complex search tasks may be supported via the search interface. In particular, we consider search tasks which may be composed of multiple aspects, or multiple related subtasks. For example, in decision making tasks the user may investigate multiple possible solutions before settling on a single, final solution, while other tasks, such as report writing, may involve searching on multiple interrelated topics.
Topic set size redux
Authors: Ellen M. Voorhees
Venue: SIGIR
Year: 2009

The cost as well as the power and reliability of a retrieval test collection are all proportional to the number of topics included in it. Test collections created through community evaluations such as TREC generally use 50 topics. Prior work estimated the reliability of 50-topic sets by extrapolating confidence levels from those of smaller sets, and concluded that 50 topics are sufficient to have high confidence in a comparison, especially when the comparison is statistically significant. Using topic sets that actually contain 50 topics, this paper shows that statistically significant differences can be wrong, even when statistical significance is accompanied by moderately large (>10%) relative differences in scores. Further, using standardized evaluation scores rather than raw evaluation scores does not increase the reliability of these paired comparisons. Researchers should continue to be skeptical of conclusions demonstrated on only a single test collection.
K-tree: large scale document clustering
Authors: Christopher M. De Vries, Shlomo Geva
Venue: SIGIR
Year: 2009

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.
Counting ancestors to estimate authority
Authors: Jian Wang, Brian D. Davison
Venue: SIGIR
Year: 2009

The AncestorRank algorithm calculates an authority score by using just one characteristic of the web graph-the number of ancestors per node. For scalability, we estimate the number of ancestors by using a probabilistic counting algorithm. We also consider the case in which ancestors which are closer to the node have more influence than those farther from the node. Thus we further apply a decay factor delta on the contributions from successively earlier ancestors. The resulting authority score is used in combination with a content-based ranking algorithm. Our experiments show that as long as delta is in the range of [0.1, 0.9], AncestorRank can greatly improve BM25 performance, and in our experiments is often better than PageRank.
Automatic URL completion and prediction using fuzzy type-ahead search
Authors: Jiannan Wang, Guoliang Li, Jianhua Feng
Venue: SIGIR
Year: 2009

Type-ahead search is a new information-access paradigm, in which systems can find answers to keyword queries "on-the-fly" as a user types in a query. It improves traditional autocomplete search by allowing query keywords to appear at different places in an answer. In this paper we study the problem of automatic URL completion and prediction using fuzzy type-ahead search. That is, we interactively find relevant URLs that contain words matching query keywords, even approximately, as the user types in a query. Supporting fuzzy search is very important when the user has limited knowledge about URLs. We describe the design and implementation of our method, and report the experimental results on Firefox.
Efficient query expansion for advertisement search
Authors: Haofen Wang, Yan Liang, Linyun Fu, Gui-Rong Xue, Yong Yu
Venue: SIGIR
Year: 2009

Online advertising represents a growing part of the revenues of ma-jor Internet service providers such as Google and Yahoo. A com-monly used strategy is to place advertisements (ads) on the search result pages according to the users ’ submitted queries. Relevant ads are likely to be clicked by a user and to increase the revenues of both advertisers and publishers. However, bid phrases defined by ad-owners are usually contained in limited number of ads. Directly matching user queries with bid phrases often results in finding few appropriate ads. To address this shortcoming, query expansion is often used to increase the chances to match the ads. Nevertheless, query expansion on top of the traditional inverted index faces ef-ficiency issues such as high time complexity and heavy I/O costs. Moreover, precision cannot always be improved, sometimes even hurt due to the involvement of additional noise.
Accommodating colorblind users in image search
Authors: Meng Wang, Bo Liu, Linjun Yang, Xian-Sheng Hua
Venue: SIGIR
Year: 2009

There are about 8% of men and 0.8% of women suffering from colorblindness. Due to certain loss of color information, the existing image search techniques may not provide satisfactory results for these users. In this demonstration, we show an image search system that can accommodate colorblind users. It can help these special users find and enjoy what they want by providing multiple services for them, including search results reranking, image recoloring and color indication.
Concept representation based video indexing
Authors: Meng Wang, Yan Song, Xian-Sheng Hua
Venue: SIGIR
Year: 2009

This poster introduces a novel concept-based video indexing approach. It is developed based on a rich set of base concepts, of which the models are available. Then, for a given concept with several labeled samples, we combine the base concepts to fit it and its model can thus be obtained accordingly. Empirical results demonstrate that this method can achieve great performance even with very limited labeled data. We have compared different representation approaches including both sparse and non-sparse methods. Our conclusion is that the sparse method will lead to much better performance.
Evolutionary document summarization for disaster management
Authors: Dingding Wang, Li Zheng, Tao Li, Yi Deng
Venue: SIGIR
Year: 2009

In this poster, we develop an evolutionary document summarization system for discovering the changes and differences in each phase of a disaster evolution. Given a collection of document streams describing an event, our system generates a short summary delivering the main development theme of the event by extracting the most representative and discriminative sentences at each phase. Experimental results on the collection of press releases for Hurricane Wilma in 2005 demonstrate the efficacy of our proposal.
Score adjustment for correction of pooling bias
Authors: William Webber, Laurence Anthony F. Park
Venue: SIGIR
Year: 2009

Information retrieval systems are evaluated against test collections of topics, documents, and assessments of which documents are relevant to which topics. Documents are chosen for relevance assessment by pooling runs from a set of existing systems. New systems can return unassessed documents, leading to an evaluation bias against them. In this paper, we propose to estimate the degree of bias against an unpooled system, and to adjust the system's score accordingly. Bias estimation can be done via leave-one-out experiments on the existing, pooled systems, but this requires the problematic assumption that the new system is similar to the existing ones. Instead, we propose that all systems, new and pooled, be fully assessed against a common set of topics, and the bias observed against the new system on the common topics be used to adjust scores on the existing topics. We demonstrate using resampling experiments on TREC test sets that our method leads to a marked reduction in error, even with only a relatively small number of common topics, and that the error decreases as the number of topics increases.
User-centric multi-criteria information retrieval
Authors: Shawn R. Wolfe, Yi Zhang
Venue: SIGIR
Year: 2009

Information retrieval models usually represent content only, and not other considerations, such as authority, cost, and recency. How could multiple criteria be utilized in information retrieval, and how would it effect the results? In our experiments, using multiple user-centric criteria always produced better results than a single criteria.