OpenRaiser/Intern-Atlas huggingface dataset:
Image sequence geolocation with human travel priors
Authors: Evangelos Kalogerakis, Olga Vesselova, James Hays, Alexei A. Efros, Aaron Hertzmann
Venue: Conference ICCV 2009
Year: 2009

This paper presents a method for estimating geographic location for sequences of time-stamped photographs. A prior distribution over travel describes the likelihood of traveling from one location to another during a given time interval. This distribution is based on a training database of 6 million photographs from Flickr.com. An image likelihood for each location is defined by matching a test photograph against the training database. Inferring location for images in a test sequence is then performed using the Forward-Backward algorithm, and the model can be adapted to individual users as well. Using temporal constraints allows our method to geolocate images without recognizable landmarks, and images with no geographic cues whatsoever. This method achieves a substantial performance improvement over the best-available baseline, and geolocates some users' images with near-perfect accuracy.
It pays to be picky: an evaluation of thread retrieval in online forums
Authors: Jonathan L. Elsas, Jaime G. Carbonell
Venue: Conference SIGIR 2009
Year: 2009

Online forums host a rich information exchange, often with contributions from many subject matter experts. In this work we evaluate algorithms for thread retrieval in a large and active online forum community. We compare methods that utilize thread structure to a naïve method that treats a thread as a single document. We find that thread structure helps, and additionally selective methods of thread scoring, which only use evidence from a small number of messages in the thread, significantly and consistently outperform inclusive methods which use all the messages in the thread.
Modeling semantics and structure of discussion threads
Authors: Chen Lin, Jiang-Ming Yang, Rui Cai, Xin-Jing Wang, Wei Wang, Lei Zhang
Venue: Conference WWW 2009
Year: 2009

The abundant knowledge in web communities has motivated the research interests in discussion threads. The dynamic nature of discussion threads poses interesting and challenging problems for computer scientists. Although techniques such as semantic models or structural models have been shown to be useful in a number of areas, they are inefficient in understanding discussion threads due to the temporal dependence among posts in a discussion thread. Such dependence causes that semantics and structure coupled with each other in discussion threads. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantic and Structure of discussion threads.
Learning effective ranking functions for newsgroup search
Authors: Wensi Xi, Jesper Lind, Eric Brill
Venue: Conference SIGIR 2004
Year: 2004

Web communities are web virtual broadcasting spaces where people can freely discuss anything. While such communities function as discussion boards, they have even greater value as large repositories of archived information. In order to unlock the value of this resource, we need an effective means for searching archived discussion threads. Unfortunately the techniques that have proven successful for searching document collections and the Web are not ideally suited to the task of searching archived community discussions. In this paper, we explore the problem of creating an effective ranking function to predict the most relevant messages to queries in community search. We extract a set of predictive features from the thread trees of newsgroup messages as well as features of message authors and lexical distribution within a message thread. Our final results indicate that when using linear regression with this feature set, our search system achieved a 28.5% performance improvement compared to our baseline system.
Language-model-based ranking for queries on rdf-graphs
Authors: Shady Elbassuoni, Maya Ramanath, Ralf Schenkel, Marcin Sydow, Gerhard Weikum
Venue: Conference CIKM 2009
Year: 2009

The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed.
Deep answers for naturally asked questions on the web of data
Authors: Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya Ramanath, Volker Tresp, Gerhard Weikum
Venue: Conference WWW 2012
Year: 2012

We present DEANNA, a framework for natural language question answering over structured knowledge bases. Given a natural language question, DEANNA translates questions into a structured SPARQL query that can be evaluated over knowledge bases such as Yago, Dbpedia, Freebase, or other Linked Data sources. DEANNA analyzes questions and maps verbal phrases to relations and noun phrases to either individual entities or semantic classes. Importantly, it judiciously generates variables for target entities or classes to express joins between multiple triple patterns. We leverage the semantic type system for entities and use constraints in jointly mapping the constituents of the question to relations, classes, and entities. We demonstrate the capabilities and interface of DEANNA, which allows advanced users to influence the translation process and to see how the different components interact to produce the final result.
Energy minimization via graph cuts: Settling what is possible
Authors: Daniel Freedman, Petros Drineas
Venue: Conference CVPR 2005
Year: 2005

The recent explosion of interest in graph cut methods in computer vision naturally spawns the question: what energy functions can be minimized via graph cuts? This question was first attacked by two papers of Kolmogorov and Zabih, in which they dealt with functions with pair-wise and triplewise pixel interactions. In this work, we extend their results in two directions. First, we examine the case of k-wise pixel interactions; the results are derived from a purely algebraic approach. Second, we discuss the applicability of provably approximate algorithms. Both of these developments should help researchers best understand what can and cannot be achieved when designing graph cut based algorithms.
Authorship attribution with thousands of candidate authors
Authors: Moshe Koppel, Jonathan Schler, Shlomo Argamon, Eran Messeri
Venue: Conference SIGIR 2006
Year: 2006

In this paper, we use a blog corpus to demonstrate that we can often identify the author of an anonymous text even where there are many thousands of candidate authors. Our approach combines standard information retrieval methods with a text categorization meta-learning scheme that determines when to even venture a guess.
Structure and semantics for expressive text kernels
Authors: Stephan Bloehdorn, Alessandro Moschitti
Venue: Conference CIKM 2007
Year: 2007

Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.
Discovering logical knowledge for deep question answering
Authors: Zhao Liu, Xipeng Qiu, Ling Cao, Xuanjing Huang
Venue: Conference CIKM 2012
Year: 2012

Most open-domain question answering systems achieve better performances with large corpora, such as Web, by taking advantage of information redundancy. However, explicit answers are not always mentioned in the corpus, many answers are implicitly contained and can only be deducted by inference. In this paper, we propose an approach to discover logical knowledge for deep question answering, which automatically extracts knowledge in an unsupervised, domain-independent manner from background texts and reasons out implicit answers for the questions. Firstly, we use semantic role labeling to transform natural language expressions to predicates in first-order logic. Then we use association analysis to uncover the implicit relations among these predicates and build propositions for inference. Since our knowledge is drawn from different sources, we use Markov logic to merge multiple knowledge bases without resolving their inconsistencies. Our experiments show that these propositions can improve the performance of question answering significantly.
Towards rich query interpretation: walking back and forth for mining query templates
Authors: Ganesh Agarwal, Govind Kabra, Kevin Chen-Chuan Chang
Venue: Conference WWW 2010
Year: 2010

We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.
Sequence clustering and labeling for unsupervised query intent discovery
Authors: Jackie Chi Kit Cheung, Xiao Li
Venue: Conference WSDM 2012
Year: 2012

One popular form of semantic search observed in several modern search engines is to recognize query patterns that trigger instant answers or domain-specific search, producing semantically enriched search results. This often requires understanding the query intent in addition to the meaning of the query terms in order to access structured data sources. A major challenge in intent understanding is to construct a domain-dependent schema and to annotate search queries based on such a schema, a process that to date has required much manual annotation effort. We present an unsupervised method for clustering queries with similar intent and for producing a pattern consisting of a sequence of semantic concepts and/or lexical items for each intent. Furthermore, we leverage the discovered intent patterns to automatically annotate a large number of queries beyond those used in clustering. We evaluated our method on 10 selected domains, discovering over 1400 intent patterns and automatically annotating 125K (and potentially many more) queries. We found that over 90% of patterns and 80% of instance annotations tested are judged to be correct by a majority of annotators.
Unsupervised extraction of template structure in web search queries
Authors: Sandeep Pandey, Kunal Punera
Venue: Conference WWW 2012
Year: 2012

Web search queries are an encoding of the user's search intent and extracting structured information from them can facilitate central search engine operations like improving the ranking of search results and advertisements. Not surprisingly, this area has attracted a lot of attention in the research community in the last few years. The problem is, however, made challenging by the fact that search queries tend to be extremely succinct; a condensation of user search needs to the bare-minimum set of keywords. In this paper we consider the problem of extracting, with no manual intervention, the hidden structure behind the observed search queries in a domain: the origins of the constituent keywords as well as the manner the individual keywords are assembled together. We formalize important properties of the problem and then give a principled solution based on generative models that satisfies these properties. Using manually labeled data we show that the query templates extracted by our solution are superior to those discovered by strong baseline methods.
Improving recommendation for long-tail queries via templates
Authors: Idan Szpektor, Aristides Gionis, Yoelle Maarek
Venue: Conference WWW 2011
Year: 2011

The ability to aggregate huge volumes of queries over a large population of users allows search engines to build precise models for a variety of query-assistance features such as query recommendation, correction, etc. Yet, no matter how much data is aggregated, the long-tail distribution implies that a large fraction of queries are rare. As a result, most query assistance services perform poorly or are not even triggered on long-tail queries. We propose a method to extend the reach of query assistance techniques (and in particular query recommendation) to long-tail queries by reasoning about rules between query templates rather than individual query transitions, as currently done in query-flow graph models. As a simple example, if we recognize that 'Montezuma' is a city in the rare query "Montezuma surf" and if the rule 'city surf → beach has been observed, we are able to offer "Montezuma beach" as a recommendation, even if the two queries were never observed in a same session. We conducted experiments to validate our hypothesis, first via traditional small-scale editorial assessments but more interestingly via a novel automated large scale evaluation methodology. Our experiments show that general coverage can be relatively increased by 24% using templates without penalizing quality. Furthermore, for 36% of the 95M queries in our query flow graph, which have no out edges and thus could not be served recommendations, we can now offer at least one recommendation in 98% of the cases.
Inference networks for document retrieval
Authors: Howard R. Turtle, W. Bruce Croft
Venue: Conference SIGIR 1990
Year: 1990

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models.
A framework for benchmarking entity-annotation systems
Authors: Marco Cornolti, Paolo Ferragina, Massimiliano Ciaramita
Venue: Conference WWW 2013
Year: 2013

In this paper we design and implement a benchmarking framework for fair and exhaustive comparison of entity-annotation systems. The framework is based upon the definition of a set of problems related to the entity-annotation task, a set of measures to evaluate systems performance, and a systematic comparative evaluation involving all publicly available datasets, containing texts of various types such as news, tweets and Web pages. Our framework is easily-extensible with novel entity annotators, datasets and evaluation measures for comparing systems, and it has been released to the public as open source. We use this framework to perform the first extensive comparison among all available entity annotators over all available datasets, and draw many interesting conclusions upon their efficiency and effectiveness. We also draw conclusions between academic versus commercial annotators.
Hiring behavior models for online labor markets
Authors: Marios Kokkodis, Panagiotis Papadimitriou, Panagiotis G. Ipeirotis
Venue: Conference WSDM 2015
Year: 2015

In an online labor marketplace employers post jobs, receive freelancer applications and make hiring decisions. These hiring decisions are based on the freelancer's observed (e.g., education) and latent (e.g., ability) characteristics. Because of the heterogeneity that appears in the observed characteristics, and the existence of latent ones, identifying and hiring the best possible applicant is a very challenging task. In this work we study and model the employer's hiring behavior. We assume that employers are utility maximizers and make rational decisions by hiring the best possible applicant at hand. Based on this premise, we propose a series of probabilistic models that estimate the hiring probability of each applicant. We train and test our models on more than 600,000 job applications obtained by oDesk.com, and we show evidence that the proposed models outperform currently in-use baselines. To get further insights, we conduct an econometric analysis and observe that the attributes that are strongly correlated with the hiring probability are whether or not the freelancer and the employer have previously worked together, the available information on the freelancer's profile, the countries of the employer and the freelancer and the skillset of the freelancer. Finally, we find that the faster a freelancer applies to an opening, the higher is the probability to get the job.
Etf: extended tensor factorization model for personalizing prediction of review helpfulness
Authors: Samaneh Moghaddam, Mohsen Jamali, Martin Ester
Venue: Conference WSDM 2012
Year: 2012

Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a user's evaluation of the review quality.
Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?
Authors: Philipp Singer, Fabian Flöck, Clemens Meinhart, Elias Zeitfogel, Markus Strohmaier
Venue: Conference WWW 2014
Year: 2014

In the past few years, Reddit -- a community-driven platform for submitting, commenting and rating links and text posts -- has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddit's evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the community's allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image- and textual content over external sources.
Investigating topic models for social media user recommendation
Authors: Marco Pennacchiotti, Siva Gurumurthy
Venue: Conference WWW 2011
Year: 2011

This paper presents a user recommendation system that recommends to a user new friends having similar interests. We automatically discover users' interests using Latent Dirichlet Allocation (LDA), a linguistic topic model that represents users as mixtures of topics. Our system is able to recommend friends for 4 million users with high recall, outperforming existing strategies based on graph analysis.
A comparison of supervised keyphrase extraction models
Authors: Florin Adrian Bulgarov, Cornelia Caragea
Venue: Conference WWW 2015
Year: 2015

Keyphrases for a document provide a high-level topic description of the document. Given the number of documents growing exponentially on the Web in the past years, accurate methods for extracting keyphrases from such documents are greatly needed. In this study, we provide a comparison of existing supervised approaches to this task to determine the current best performing model. We use research articles on the Web as the case study.
Less is More: Filtering Abnormal Dimensions in GloVe
Authors: Yang-Yin Lee, Hao Ke, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference WWW 2016
Year: 2016

GloVe, global vectors for word representation, performs well in some word analogy and semantic relatedness tasks. However, we find that some dimensions of the trained word embedding are abnormal. We verify our conjecture via removing these abnormal dimensions using Kolmogorov-Smimov test and experiment on several benchmark datasets for semantic relatedness measurement. The experimental results confirm our finding. Interestingly, some of the tasks outperform the state-of-the-art model SensEmbed by simply removing these abnormal dimensions. The novel rule of thumb technique which leads to better performance is expected to be useful in practice.
Position-aware representations for relevance matching in neural information retrieval
Authors: Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo
Venue: Conference WWW 2017
Year: 2017

To deploy deep learning models for ad-hoc information retrieval, suitable representations of query-document pairs are needed. Such representations ought to capture all relevant information required to assess the relevance of a document for a given query, including uni-gram term overlap as well as positional information such as proximity and term dependencies. In this work, we investigate the use of similarity matrices that are able to encode such position-specific information. Extensive experiments on TREC Web Track data confirm that such representations can yield good results.
Ranking from pairs and triplets: information quality, evaluation methods and query complexity
Authors: Kira Radinsky, Nir Ailon
Venue: Conference WSDM 2011
Year: 2011

Obtaining judgments from human raters is a vital part in the design of search engines' evaluation. Today, a discrepancy exists between judgment acquisition from raters (training phase) and use of the responses for retrieval evaluation (evaluation phase). This discrepancy is due to the inconsistency between the representation of the information in both phases. During training, raters are requested to provide a relevance score for an individual result in the context of a query, whereas the evaluation is performed on ordered lists of search results, with the results' relative position (compared to other results) taken into account. As an alternative to the practice of learning to rank using relevance judgments for individual search results, more and more focus has recently been diverted to the theory and practice of learning from answers to combinatorial questions about sets of search results. That is, users, during training, are asked to rank small sets (typically pairs).