OpenRaiser/Intern-Atlas huggingface dataset:
A novel topic model for automatic term extraction
Authors: Sujian Li, Jiwei Li, Tao Song, Wenjie Li, Baobao Chang
Venue: SIGIR
Year: 2013

Automatic term extraction (ATE) aims at extracting domain-specific terms from a corpus of a certain domain. Termhood is one essential measure for judging whether a phrase is a term. Previous researches on termhood mainly depend on the word frequency information. In this paper, we propose to compute termhood based on semantic representation of words. A novel topic model, namely i-SWB, is developed to map the domain corpus into a latent semantic space, which is composed of some general topics, a background topic and a documents-specific topic. Experiments on four domains demonstrate that our approach outperforms the state-of-the-art ATE approaches.
Exploiting hybrid contexts for Tweet segmentation
Authors: Chenliang Li, Aixin Sun, Jianshu Weng, Qi He
Venue: SIGIR
Year: 2013

Twitter has attracted hundred millions of users to share and disseminate most up-to-date information. However, the noisy and short nature of tweets makes many applications in information retrieval (IR) and natural language processing (NLP) challenging. Recently, segment-based tweet representation has demonstrated effectiveness in named entity recognition (NER) and event detection from tweet streams. To split tweets into meaningful phrases or segments, the previous work is purely based on external knowledge bases, which ignores the rich local context information embedded in the tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. HybridSeg incorporates local context knowledge with global knowledge bases for better tweet segmentation. HybridSeg consists of two steps: learning from off-the-shelf weak NERs and learning from pseudo feedback. In the first step, the existing NER tools are applied to a batch of tweets. The named entities recognized by these NERs are then employed to guide the tweet segmentation process. In the second step, HybridSeg adjusts the tweet segmentation results iteratively by exploiting all segments in the batch of tweets in a collective manner. Experiments on two tweet datasets show that HybridSeg significantly improves tweet segmentation quality compared with the state-of-the-art algorithm. We also conduct a case study by using tweet segments for the task of named entity recognition from tweets. The experimental results demonstrate that HybridSeg significantly benefits the downstream applications.
A query and patient understanding framework for medical records search
Authors: Nut Limsopatham
Venue: SIGIR
Year: 2013

Electronic medical records (EMRs) are being increasingly used worldwide to facilitate improved healthcare services [2,3]. They describe the clinical decision process relating to a patient, detailing the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. However, medical records search is challenging, due to the implicit knowledge inherent within the medical records - such knowledge may be known by medical practitioners, but hidden to an information retrieval (IR) system [3]. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitly mentioned in the patient's EMRs. Moreover, the fact that a symptom has not been observed by a clinician may rule out some specific diagnoses.
InfoLand: information lay-of-land for session search
Authors: Jiyun Luo, Dongyi Guan, Hui Yang
Venue: SIGIR
Year: 2013

Search result clustering (SRC) is a post-retrieval process that hierarchically organizes search results. The hierarchical structure offers overview for the search results and displays an "information lay-of-land" that intents to guide the users throughout a search session. However, SRC hierarchies are sensitive to query changes, which are common among queries in the same session. This instability may leave users seemly random overviews throughout the session. We present a new tool called InfoLand that integrates external knowledge from Wikipedia when building SRC hierarchies and increase their stability. Evaluation on TREC 2010-2011 Session Tracks shows that InfoLand produces more stable results organization than a commercial search engine.
TweetMogaz: a news portal of tweets
Authors: Walid Magdy
Venue: SIGIR
Year: 2013

Twitter is currently one of the largest social hubs for users to spread and discuss news. For most of the top news stories happening, there are corresponding discussions on social media. In this demonstration TweetMogaz is presented, which is a platform for microblog search and filtering. It creates a real-time comprehensive report about what people discuss and share around news happening in certain regions. TweetMogaz reports the most popular tweets, jokes, videos, images, and news articles that people share about top news stories. Moreover, it allows users to search for specific topics. A scalable automatic technique for microblog filtering is used to obtain relevant tweets to a certain news category in a region. TweetMogaz.com demonstrates the effectiveness of our filtering technique for reporting public response toward news in different Arabic regions including Egypt and Syria in real-time.
Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval
Authors: Parvaz Mahdabi, Shima Gerani, Jimmy Xiangji Huang, Fabio Crestani
Venue: SIGIR
Year: 2013

Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation.
News vertical search: when and what to display to users
Authors: Richard McCreadie, Craig Macdonald, Iadh Ounis
Venue: SIGIR
Year: 2013

News reporting has seen a shift toward fast-paced online reporting in new sources such as social media. Web Search engines that support a news vertical have historically relied upon articles published by major newswire providers when serving news-related queries. In this paper, we investigate to what extent real-time content from newswire, blogs, Twitter and Wikipedia sources are useful to return to the user in the current fast-paced news search setting. In particular, we perform a detailed user study using the emerging medium of crowdsourcing to determine when and where integrating news-related content from these various sources can better serve the user's news need. We sampled approximately 300 news-related search queries using Google Trends and Bitly data in real-time for two time periods. For these queries, we have crowdsourced workers compare Web search rankings for each, with similar rankings integrating real-time news content from sources such as Twitter or the blogosphere. Our results show that users exhibited a preference for rankings integrating newswire articles for only half of our queries, indicating that relying solely on newswire providers for news-related content is now insufficient. Moreover, our results show that users preferred rankings that integrate tweets more often than those that integrate newswire articles, showing the potential of using social media to better serve news queries.
On contextual photo tag recommendation
Authors: Philip J. McParlane, Yashar Moshfeghi, Joemon M. Jose
Venue: SIGIR
Year: 2013

Image tagging is a growing application on social media websites, however, the performance of many auto-tagging methods are often poor. Recent work has exploited an image's context (e.g. time and location) in the tag recommendation process, where tags which co-occur highly within a given time interval or geographical area are promoted. These models, however, fail to address how and when different image contexts can be combined. In this paper, we propose a weighted tag recommendation model, building on an existing state-of-the-art, which varies the importance of time and location in the recommendation process, based on a given set of input tags. By retrieving more temporally and geographically relevant tags, we achieve statistically significant improvements to recommendation accuracy when testing on 519k images collected from Flickr. The result of this paper is an important step towards more effective image annotation and retrieval systems.
Entity linking and retrieval
Authors: Edgar Meij, Krisztian Balog, Daan Odijk
Venue: SIGIR
Year: 2013

This full-day tutorial presents a comprehensive introduction to entity linking and retrieval. Part I provides a detailed overview of entity linking: identifying and disambiguating entity occurrences in unstructured text. Part II focuses on entity retrieval, by first considering scenarios where explicit representations of entities are available, and then moving to a setting where evidence needs to be collected and aggregated from multiple documents or even collections, thereby combining techniques from both entity linking and entity retrieval. Part III concludes the tutorial with an overview and hands-on comparative analysis of applications and publicly available toolkits and web services.
Time-aware structured query suggestion
Authors: Taiki Miyanishi, Tetsuya Sakai
Venue: SIGIR
Year: 2013

Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.
Semantic models for answer re-ranking in question answering
Authors: Piero Molino
Venue: SIGIR
Year: 2013

The task of Question Answering (QA) is to find correct answers to users' questions expressed in natural language. In the last few years non-factoid QA received more attention. It focuses on causation, manner and reason questions, where the expected answer has the form of a passage of text. The presence of question and answers corpora allows the adoption of Learning to Rank (MLR) algorithms in order to out- put a sensible ranking of the candidate answers. The importance and effectiveness of linguistically motivated features, obtained from syntax, lexical semantics and semantic role labeling, was shown in literature [2-4], but there are still several different possible semantic features that have not been taken into account so far and our goal is to find out if their use could lead to performance improvement. In particular features coming from Semantic Models (SM) like Distributional Semantic Models (DSMs), Explicit Semantic Analysis (ESA), Latent Dirichlet Allocation (LDA) induced topics have never been applied to the task so far. Based on the usefulness that those models show in other tasks, we think that SM can have a significant role in improving current state-of-the-art systems' performance in answer re-ranking.
Kinship contextualization: utilizing the preceding and following structural elements
Authors: Muhammad Ali Norozi, Paavo Arvola
Venue: SIGIR
Year: 2013

The textual context of an element, structurally, contains traces of evidences. Utilizing this context in scoring is called contextualization. In this study we hypothesize that the context of an XML-element originated from its \textit{preceding} and \textit{following} elements in the sequential ordering of a document improves the quality of retrieval. In the tree form of the document's structure, \textit{kinship} contextualization means, contextualization based on the horizontal and vertical elements in the \textit{kinship tree,} or elements in closer to a wider structural kinship. We have tested several variants of kinship contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of focused elements.
Flex-BaseX: an XML engine with a flexible extension of Xquery full-text
Authors: Emanuele Panzeri, Gabriella Pasi
Venue: SIGIR
Year: 2013

XML is the most used language for structuring data and documents, besides being the de-facto standard for data exchange. Keyword based search has been implemented by the XQuery Full-Text language extension, allowing document fragments to be retrieved and ranked via keyword-based matching in the Information Retrieval style. In this demo the implementation of an XQuery extension allowing users to express their vague knowledge of the underlying XML structure is presented. The integration has been performed on top of the BaseX query engine; the work, as initially done by Panzeri at al. in IIR 2013 as a proof-of-concept has been further enhanced and extended.
The knowing camera: recognizing places-of-interest in smartphone photos
Authors: Pai Peng, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
Venue: SIGIR
Year: 2013

This paper presents a framework called Knowing Camera for real-time recognizing places-of-interest in smartphone photos, with the availability of online geotagged images of such places. We propose a probabilistic field-of-view model which captures the uncertainty in camera sensor data. This model can be used to retrieve a set of candidate images. The visual similarity computation of the candidate images relies on the sparse coding technique. We also propose an ANN filtering technique to speedup the sparse coding. The final ranking combines an uncertain geometric relevance with the visual similarity. Our preliminary experiments conducted in an urban area of a large city show promising results. The most distinguishing feature of our framework is its ability to perform well in contaminated, real-world online image database. Besides, our framework is highly scalable as it does not incur any complex data structure.
Looking ahead: query preview in exploratory search
Authors: Pernilla Qvarfordt, Gene Golovchinsky, Tony Dunnigan, Elena Agapie
Venue: SIGIR
Year: 2013

Exploratory search is a complex, iterative information seeking activity that involves running multiple queries and finding and examining many documents. We designed a query preview control that visualizes the distribution of newly-retrieved and re-retrieved documents prior to running the query. When evaluating the preview control with a control condition, we found effects on both people's information seeking behavior and improved retrieval performance. People spent more time formulating a query and were more likely to explore search results more deeply, retrieved a more diverse set of documents, and found more different relevant documents when using the preview.
Shame to be sham: addressing content-based grey hat search engine optimization
Authors: Fiana Raiber, Kevyn Collins-Thompson, Oren Kurland
Venue: SIGIR
Year: 2013

We present an initial study identifying a form of content-based grey hat search engine optimization, in which a Web page contains both potentially relevant content and manipulated content: we call such pages sham documents, because they lie in the grey area between 'ham' (clearly normal) and 'spam' (clearly fake). Sham documents are often ranked artificially high in response to certain queries, but also may contain some useful information and cannot be considered as absolute spam. We report a novel annotation effort performed with the ClueWeb09 benchmark where pages were labeled as being spam, sham, or legitimate content. Significant inter-annotator agreement rates support the claim that there are sham documents that are highly ranked by a very effective retrieval approach, yet are not spam. We also present an initial study of predictors that may indicate whether a query is the target of shamming.
Ranking document clusters using markov random fields
Authors: Fiana Raiber, Oren Kurland
Venue: SIGIR
Year: 2013

An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types of cluster-relevance evidence; e.g., the query-similarity values of the cluster's documents and query-independent measures of the cluster. We use our method to re-rank an initially retrieved document list by ranking clusters that are created from the documents most highly ranked in the list. The resultant retrieval effectiveness is substantially better than that of the initial list for several lists that are produced by effective retrieval methods. Furthermore, our cluster ranking approach significantly outperforms state-of- the-art cluster ranking methods. We also show that our method can be used to improve the performance of (state-of- the-art) results-diversification methods.
Toward whole-session relevance: exploring intrinsic diversity in web search
Authors: Karthik Raman, Paul N. Bennett, Kevyn Collins-Thompson
Venue: SIGIR
Year: 2013

Current research on web search has focused on optimizing and evaluating single queries. However, a significant fraction of user queries are part of more complex tasks [20] which span multiple queries across one or more search sessions [26,24]. An ideal search engine would not only retrieve relevant results for a user's particular query but also be able to identify when the user is engaged in a more complex task and aid the user in completing that task [29,1]. Toward optimizing whole-session or task relevance, we characterize and address the problem of intrinsic diversity (ID) in retrieval [30], a type of complex task that requires multiple interactions with current search engines. Unlike existing work on extrinsic diversity [30] that deals with ambiguity in intent across multiple users, ID queries often have little ambiguity in intent but seek content covering a variety of aspects on a shared theme. In such scenarios, the underlying needs are typically exploratory, comparative, or breadth-oriented in nature. We identify and address three key problems for ID retrieval: identifying authentic examples of ID tasks from post-hoc analysis of behavioral signals in search logs; learning to identify initiator queries that mark the start of an ID search task; and given an initiator query, predicting which content to prefetch and rank.
#trapped!: social media search system requirements for emergency management professionals
Authors: Stefan Raue, Leif Azzopardi, Christopher W. Johnson
Venue: SIGIR
Year: 2013

Social media provides a new and potentially rich source of information for emergency management services. However, extracting the relevant information from such streams poses a number of difficult challenges. In this short paper, we survey emergency management professionals to ascertain how social media is used when responding to incidents, the search strategies that they undertake, and the challenges that they face when using social media streams. This research indicates that emergency management professionals employ two main strategies when searching social media streams: keyword-centric and account-centric search strategies. Furthermore, current search interfaces are inadequate regarding the requirements of command and control environments in the emergency management domain, where the process of information seeking is collaborative in nature and needs to support multiple information seekers.
What can pictures tell us about web pages?: improving document search using images
Authors: Sergio Rodríguez-Vaamonde, Lorenzo Torresani, Andrew W. Fitzgibbon
Venue: SIGIR
Year: 2013

Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of an HTML document and consequently boost the performance of a keyword-based search engine. We present a Web-scalable system that exploits a pure text-based search engine to find an initial set of candidate documents for a given query. Then, the candidate set is reranked using semantic information extracted from the images contained in the pages. The resulting system retains the computational efficiency of traditional text-based search engines with only a small additional storage cost needed to encode the visual information. We test our approach on the TREC 2009 Million Query Track, where we show that our use of visual content yields improvement in accuracies for two distinct text-based search engines, including the system with the best reported performance on this benchmark.
Modeling the uniqueness of the user preferences for recommendation systems
Authors: Haggai Roitman, David Carmel, Yosi Mass, Iris Eiron
Venue: SIGIR
Year: 2013

In this paper we propose a novel framework for modeling the uniqueness of the user preferences for recommendation systems. User uniqueness is determined by learning to what extent the user's item preferences deviate from those of an "average user" in the system. Based on this framework, we suggest three different recommendation strategies that trade between uniqueness and conformity. Using two real item datasets, we demonstrate the effectiveness of our uniqueness based recommendation framework.
ThemeStreams: visualizing the stream of themes discussed in politics
Authors: Ork de Rooij, Daan Odijk, Maarten de Rijke
Venue: SIGIR
Year: 2013

The political landscape is fluid. Discussions are always ongoing and new "hot topics" continue to appear in the headlines. But what made people start talking about that topic? And who started it? Because of the speed at which discussions sometimes take place this can be difficult to track down. We describe ThemeStreams: a demonstrator that maps political discussions to themes and influencers and illustrate how this mapping is used in an interactive visualization that shows us which themes are being discussed, and that helps us answer the question "Who put this issue on the map?" in streams of political data.