OpenRaiser/Intern-Atlas huggingface dataset:
Understanding negation and family history to improve clinical information retrieval
Authors: Bevan Koopman, Guido Zuccon
Venue: SIGIR
Year: 2014

We present a study to understand the effect that negated terms (e.g., "no fever") and family history (e.g., "family his- tory of diabetes") have on searching clinical records. Our analysis is aimed at devising the most effective means of handling negation and family history. In doing so, we explicitly represent a clinical record according to its different content types: negated, family history and normal content; the retrieval model weights each of these separately. Empirical evaluation shows that overall the presence of negation harms retrieval effectiveness while family history has little effect. We show negation is best handled by weighting negated content (rather than the common practise of re- moving or replacing it). However, we also show that many queries benefit from the inclusion of negated content and that negation is optimally handled on a per-query basis. Additional evaluation shows that adaptive handing of negated and family history content can have significant benefits.
Relevation!: an open source system for information retrieval relevance assessment}
Authors: Bevan Koopman, Guido Zuccon
Venue: SIGIR
Year: 2014

Relevation! is a system for performing relevance judgements for information retrieval evaluation. Relevation! is web-based, fully configurable and expandable; it allows researchers to effectively collect assessments and additional qualitative data. The system is easily deployed allowing assessors to smoothly perform their relevance judging tasks, even remotely. Relevation! is available as an open source project at: http://ielab.github.io/relevation.
Where not to go?: detecting road hazards using twitter
Authors: Avinash Kumar, Miao Jiang, Yi Fang
Venue: SIGIR
Year: 2014

Conventional approaches to road hazard detection involve manual inspections of roads by government transportation agencies. These approaches are usually expensive to execute, and sometimes are not able to capture the most recent hazards. Moreover, they often only focus on major highways due to a lack of sufficient manpower. Consequently, many hazards on minor roads get ignored, which may pose serious dangers to drivers. In this paper, we demonstrate an application of Twitter to atomically determining road hazards. By building language models based on Twitter users' online communication, our system aims at pinpointing potential road hazards that pose driving risks. The likelihood of poor driving conditions can then be exposed via map overlays to warn drivers about potentially dangerous driving conditions in their locale or on current routes, thereby significantly reducing the chances of an accident occurring. To the best of our knowledge, this is the first work demonstrating the utility of social media to automatically detect road hazards. We conduct experiments on a testbed of tweets discussing road conditions and the initial results demonstrate the effectiveness of our approach.
Characterizing multi-click search behavior and the risks and opportunities of changing results during use
Authors: Chia-Jung Lee, Jaime Teevan, Sebastian de la Chica
Venue: SIGIR
Year: 2014

Although searchers often click on more than one result following a query, little is known about how they interact with search results after their first click. Using large scale query log analysis, we characterize what people do when they return to a result page after having visited an initial result. We find that the initial click provides insight into the searcher's subsequent behavior, with short initial dwell times suggesting more future interaction and later clicks occurring close in rank to the first. Although users think of a search result list as static, when people return to a result list following a click there is the opportunity for the list to change, potentially providing additional relevant content. Such change, however, can be confusing, leading to increased abandonment and slower subsequent clicks. We explore the risks and opportunities of changing search results during use, observing, for example, that when results change above a user's initial click that user is less likely to find new content, whereas changes below correlate with increased subsequent interaction. Our results can be used to improve people's search experience during the course of a single query by seamlessly providing new, more relevant content as the user interacts with a search result page, helping them find what they are looking for without having to issue a new query.
Random subspace for binary codes learning in large scale image retrieval
Authors: Cong Leng, Jian Cheng, Hanqing Lu
Venue: SIGIR
Year: 2014

Due to the fast query speed and low storage cost, hashing based approximate nearest neighbor search methods have attracted much attention recently. Many state of the art methods are based on eigenvalue decomposition. In these approaches, the information caught in different dimensions is unbalanced and generally most of the information is contained in the top eigenvectors. We demonstrate that this leads to an unexpected phenomenon that longer hashing code does not necessarily yield better performance. In this work, we introduce a random subspace strategy to address this limitation. At first, a small fraction of the whole feature space is randomly sampled to train the hashing algorithms each time and only the top eigenvectors are kept to generate one piece of short code. This process will be repeated several times and then the obtained many pieces of short codes are concatenated into one piece of long code. Theoretical analysis and experiments on two benchmarks confirm the effectiveness of the proposed strategy for hashing.
Fine-grained location extraction from tweets with temporal awareness
Authors: Chenliang Li, Aixin Sun
Venue: SIGIR
Year: 2014

Twitter is a popular platform for sharing activities, plans, and opinions. Through tweets, users often reveal their location information and short term visiting plans. In this paper, we are interested in extracting fine-grained locations mentioned in tweets with temporal awareness. More specifically, we like to extract each point-of-interest (POI) mention in a tweet and predict whether the user has visited, is currently at, or will soon visit this POI. Our proposed solution, named PETAR, consists of two main components: a POI inventory and a time-aware POI tagger. The POI inventory is built by exploiting the crowd wisdom of Foursquare community. It contains not only the formal names of POIs but also the informal abbreviations. The POI tagger, based on Conditional Random Field (CRF) model, is designed to simultaneously identify the POIs and resolve their associated temporal awareness. In our experiments, we investigated four types of features (i.e., lexical, grammatical, geographical, and BILOU schema features) for time-aware POI extraction. With the four types of features, PETAR achieves promising extraction accuracy and outperforms all baseline methods.
A revisit to social network-based recommender systems
Authors: Hui Li, Dingming Wu, Nikos Mamoulis
Venue: SIGIR
Year: 2014

With the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS.
ReQ-ReC: high recall retrieval with query pooling and interactive classification
Authors: Cheng Li, Yue Wang, Paul Resnick, Qiaozhu Mei
Venue: SIGIR
Year: 2014

We consider a scenario where a searcher requires both high precision and high recall from an interactive retrieval process. Such scenarios are very common in real life, exemplified by medical search, legal search, market research, and literature review. When access to the entire data set is available, an active learning loop could be used to ask for additional relevance feedback labels in order to refine a classifier. When data is accessed via search services, however, only limited subsets of the corpus can be considered, subsets defined by queries. In that setting, relevance feedback has been used in a query enhancement loop that updates a query.
Fusion helps diversification
Authors: Shangsong Liang, Zhaochun Ren, Maarten de Rijke
Venue: SIGIR
Year: 2014

A popular strategy for search result diversification is to first retrieve a set of documents utilizing a standard retrieval method and then rerank the results. We adopt a different perspective on the problem, based on data fusion. Starting from the hypothesis that data fusion can improve performance in terms of diversity metrics, we examine the impact of standard data fusion methods on result diversification. We take the output of a set of rankers, optimized for diversity or not, and find that data fusion can significantly improve state-of-the art diversification methods. We also introduce a new data fusion method, called diversified data fusion, which infers latent topics of a query using topic modeling, without leveraging outside information. Our experiments show that data fusion methods can enhance the performance of diversification and DDF significantly outperforms existing data fusion methods in terms of diversity metrics.
New and improved: modeling versions to improve app recommendation
Authors: Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, Tat-Seng Chua
Venue: SIGIR
Year: 2014

Existing recommender systems usually model items as static -- unchanging in attributes, description, and features. However, in domains such as mobile apps, a version update may provide substantial changes to an app as updates, reflected by an increment in its version number, may attract a consumer's interest for a previously unappealing version. Version descriptions constitute an important recommendation evidence source as well as a basis for understanding the rationale for a recommendation. We present a novel framework that incorporates features distilled from version descriptions into app recommendation. We use a semi-supervised topic model to construct a representation of an app's version as a set of latent topics from version metadata and textual descriptions. We then discriminate the topics based on genre information and weight them on a per-user basis to generate a version-sensitive ranked list of apps for a target user. Incorporating our version features with state-of-the-art individual and hybrid recommendation techniques significantly improves recommendation quality. An important advantage of our method is that it targets particular versions of apps, allowing previously disfavored apps to be recommended when user-relevant features are added.
Probabilistic ensemble learning for vietnamese word segmentation
Authors: Wuying Liu, Li Lin
Venue: SIGIR
Year: 2014

Word segmentation is a challenging issue, and the corresponding algorithms can be used in many applications of natural language processing. This paper addresses the problem of Vietnamese word segmentation, proposes a probabilistic ensemble learning (PEL) framework, and designs a novel PEL-based word segmentation (PELWS) algorithm. Supported by the data structure of syllable-syllable frequency index, the PELWS algorithm combines multiple weak segmenters to form a strong segmenter within the PEL framework. The experimental results show that the PELWS algorithm can achieve the state-of-the-art performance in the Vietnamese word segmentation task.
CiteSight: supporting contextual citation recommendation using differential search
Authors: Avishay Livne, Vivek Gokuladas, Jaime Teevan, Susan T. Dumais, Eytan Adar
Venue: SIGIR
Year: 2014

A person often uses a single search engine for very different tasks. For example, an author editing a manuscript may use the same academic search engine to find the latest work on a particular topic or to find the correct citation for a familiar article. The author's tolerance for latency and accuracy may vary according to task. However, search engines typically employ a consistent approach for processing all queries. In this paper we explore how a range of search needs and expectations can be supported within a single search system using differential search. We introduce CiteSight, a system that provides personalized citation recommendations to author groups that vary based on task. CiteSight presents cached recommendations instantaneously for online tasks (e.g., active paper writing), and refines these recommendations in the background for offline tasks (e.g., future literature review). We develop an active cache-warming process to enhance the system as the author works, and context-coupling, a technique for augment sparse citation networks. By evaluating the quality of the recommendations and collecting user feedback, we show that differential search can provide a high level of accuracy for different tasks on different time scales. We believe that differential search can be used in many situations where the user's tolerance for latency and desired response vary dramatically based on use.
Web search without 'stupid' results
Authors: Aleksandra Lomakina, Nikita Povarov, Pavel Serdyukov
Venue: SIGIR
Year: 2014

One of the main targets of any search engine is to make every user fully satisfied with her search results. For this reason, lots of efforts are being paid to improving ranking models in order to show the best results to users. However, there is a class of documents on the Web, which can spoil all efforts being shown to the users. When users receive results, which are not only irrelevant, but also completely out of the picture of their expectations, they can get really frustrated. So, we attempted to find a method to determine such documents and reduce their negative impact upon users and, as a consequence, on search engines in general.
Economically-efficient sentiment stream analysis
Authors: Roberto Lourenço de Oliveira Jr., Adriano Veloso, Adriano C. M. Pereira, Wagner Meira Jr., Renato Ferreira, Srinivasan Parthasarathy
Venue: SIGIR
Year: 2014

Text-based social media channels, such as Twitter, produce torrents of opinionated data about the most diverse topics and entities. The analysis of such data (aka. sentiment analysis) is quickly becoming a key feature in recommender systems and search engines. A prominent approach to sentiment analysis is based on the application of classification techniques, that is, content is classified according to the attitude of the writer. A major challenge, however, is that Twitter follows the data stream model, and thus classifiers must operate with limited resources, including labeled data and time for building classification models. Also challenging is the fact that sentiment distribution may change as the stream evolves. In this paper we address these challenges by proposing algorithms that select relevant training instances at each time step, so that training sets are kept small while providing to the classifier the capabilities to suit itself to, and to recover itself from, different types of sentiment drifts. Simultaneously providing capabilities to the classifier, however, is a conflicting-objective problem, and our proposed algorithms employ basic notions of Economics in order to balance both capabilities. We performed the analysis of events that reverberated on Twitter, and the comparison against the state-of-the-art reveals improvements both in terms of error reduction (up to 14%) and reduction of training resources (by orders of magnitude).
Computing and applying topic-level user interactions in microblog recommendation
Authors: Xiao Lu, Peng Li, Hongyuan Ma, Shuxin Wang, Anying Xu, Bin Wang
Venue: SIGIR
Year: 2014

With the development of microblog services, tens of thousands of messages are produced every day and recommending useful messages according to users' interest is recognized as an effective way to overcome the information overload problem. Collaborative filtering which rooted from recommender system has been utilized for microblog recommendation, where social relationship information can help improve the recommendation performance. However, most of existing methods only consider the static relationship, i.e. the following relationship, which totally ignores the relationship conveyed by users' repost behaviors. To explore the effects of behavior based relationship on recommendation, we propose an Interaction Based Collaborative Filtering (IBCF) approach. Specifically, we first use topic model to analyze users' interactive behaviors and measure the topic-specific relationship strength, then we incorporate the relationship factor into the matrix factorization framework. Experimental results show that compared to the current popular social recommendation methods, IBCF can achieve better performance on the MAP and NDCG evaluation measures, and have better interpretability for the recommended results.
A collective topic model for milestone paper discovery
Authors: Ziyu Lu, Nikos Mamoulis, David W. Cheung
Venue: SIGIR
Year: 2014

Prior arts stay at the foundation for future work in academic research. However the increasingly large amount of publications makes it difficult for researchers to effectively discover the most important previous works to the topic of their research. In this paper, we study the automatic discovery of the core papers for a research area. We propose a collective topic model on three types of objects: papers, authors and published venues. We model any of these objects as bags of citations. Based on Probabilistic latent semantic analysis (PLSA), authorship, published venues and citation relations are used for quantifying paper importance. Our method discusses milestone paper discovery in different cases of input objects. Experiments on the ACL Anthology Network (ANN) indicate that our model is superior in milestone paper discovery when compared to a previous model which considers only papers.
PAAP: prefetch-aware admission policies for query results cache in web search engines
Authors: Hongyuan Ma, Wei Liu, Bingjie Wei, Liang Shi, Xiuguo Bao, Lihong Wang, Bin Wang
Venue: SIGIR
Year: 2014

Caching query results is an efficient technique for Web search engines. Admission policy can prevent infrequent queries from taking space of more frequent queries in the cache. In this paper we present two novel admission policies tailored for query results cache. These policies are based on query results prefetching information. We also propose a demote operation for the query results cache to improve the cache hit ratio. We then use a trace of over 5 million queries to evaluate our admission policies, as well as traditional policies. Experimental results show that our prefetch-aware admission policies can achieve hit ratios better than state-of-the-art admission policies.
An interactive interface for visualizing events on Twitter
Authors: Andrew James McMinn, Daniel Tsvetkov, Tsvetan R. Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose
Venue: SIGIR
Year: 2014

In recent years, social media has become one of the most popular tools for discovering and following breaking news and ongoing events. However tools and interfaces have lagged behind users' expectations, with current tools making it difficult to discover new events and failing to provide a solution to the problem of information overload. We have developed an interactive interface for visualizing events, backed by a state-of-the-art event detection approach, which is able to detect, track and summarize events in real-time. Our interface provides up-to-the-second information about ongoing events in an easy to understand manner, including category information, temporal distribution, and location information -- all of which was previously unobtainable in real-time.
Exploiting Twitter and Wikipedia for the annotation of event images
Authors: Philip James McParlane, Joemon M. Jose
Venue: SIGIR
Year: 2014

With the rise in popularity of smart phones, there has been a recent increase in the number of images taken at large social (e.g. festivals) and world (e.g. natural disasters) events which are uploaded to image sharing websites such as Flickr. As with all online images, they are often poorly annotated, resulting in a difficult retrieval scenario. To overcome this problem, many photo tag recommendation methods have been introduced, however, these methods all rely on historical Flickr data which is often problematic for a number of reasons, including the time lag problem (i.e. in our collection, users upload images on average 50 days after taking them, meaning "training data" is often out of date). In this paper, we develop an image annotation model which exploits textual content from related Twitter and Wikipedia data which aims to overcome the discussed problems. The results of our experiments show and highlight the merits of exploiting social media data for annotating event images, where we are able to achieve recommendation accuracy comparable with a state-of-the-art model.
A novel system for the semi automatic annotation of event images
Authors: Philip James McParlane, Joemon M. Jose
Venue: SIGIR
Year: 2014

With the rise in popularity of smart phones, taking and sharing photographs has never been more openly accessible. Further, photo sharing websites, such as Flickr, have made the distribution of photographs easy, resulting in an increase of visual content uploaded online. Due to the laborious nature of annotating images, however, a large percentage of these images are unannotated making their organisation and retrieval difficult. Therefore, there has been a recent research focus on the automatic and semi-automatic process of annotating these images. Despite the progress made in this field, however, annotating images automatically based on their visual appearance often results in unsatisfactory suggestions and as a result these models have not been adopted in photo sharing websites. Many methods have therefore looked to exploit new sources of evidence for annotation purposes, such as image context for example. In this demonstration, we instead explore the scenario of annotating images taken at a large scale events where evidences can be extracted from a wealth of online textual resources. Specifically, we present a novel tag recommendation system for images taken at a popular music festival which allows the user to select relevant tags from related Tweets and Wikipedia content, thus reducing the workload involved in the annotation process.
Old dogs are great at new tricks: column stores for ir prototyping
Authors: Hannes Mühleisen, Thaer Samar, Jimmy Lin, Arjen P. de Vries
Venue: SIGIR
Year: 2014

We make the suggestion that instead of implementing custom index structures and query evaluation algorithms, IR researchers should simply store document representations in a column-oriented relational database and implement ranking models using SQL. For rapid prototyping, this is particularly advantageous since researchers can explore new scoring functions and features by simply issuing SQL queries, without needing to write imperative code. We demonstrate the feasibility of this approach by an implementation of conjunctive BM25 using two modern column stores. Experiments on a web collection show that a retrieval engine built in this manner achieves effectiveness and efficiency on par with custom-built retrieval engines, but provides many additional advantages, including cleaner query semantics, a simpler architecture, built-in support for error analysis, and the ability to exploit advances in database technology "for free".