OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing human actions using multiple features
Authors: Jingen Liu, Saad Ali, Mubarak Shah
Venue: Conference CVPR 2008
Year: 2008

In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (or cuboids), and ii) a quantized vocabulary of spin-images, which aims to capture the shape deformation of the actor by considering actions as 3D objects (x, y, t). To optimally combine these features, we treat different features as nodes in a graph, where weighted edges between the nodes represent the strength of the relationship between entities. The graph is then embedded into a k-dimensional space subject to the criteria that similar nodes have Euclidian coordinates which are closer to each other. This is achieved by converting this constraint into a minimization problem whose solution is the eigenvectors of the graph Laplacian matrix. This procedure is known as Fiedler Embedding. The performance of the proposed framework is tested on publicly available data sets. The results demonstrate that fusion of multiple features helps in achieving improved performance, and allows retrieval of meaningful features and videos from the embedding space.
Learning motion categories using both semantic and structural information
Authors: Shu-Fai Wong, Tae-Kyun Kim 0001, Roberto Cipolla
Venue: Conference CVPR 2007
Year: 2007

Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotemporal interest points (part-based approach). Holistic approaches tend to be more sensitive to noise e.g. geometric variations, while part-based approaches usually ignore structural dependencies between parts. This paper presents a novel generative model, which extends probabilistic latent semantic analysis (pLSA), to capture both semantic (content of parts) and structural (connection between parts) information for motion category recognition. The structural information learnt can also be used to infer the location of motion for the purpose of motion detection. We test our algorithm on challenging datasets involving human actions, facial expressions and hand gestures and show its performance is better than existing unsupervised methods in both tasks of motion localisation and recognition.
An Experimental Framework for Email Categorization and Management
Authors: Kenrick J. Mock
Venue: Conference SIGIR 2001
Year: 2001

Many problems are difficult to adequately explore until a prototype exists in order to elicit user feedback. One such problem is a system that automatically categorizes and manages email. Due to a myriad of user interface issues, a prototype is necessary to determine what techniques and technologies are effective in the email domain. This paper describes the implementation of an add-in for Microsoft Outlook 2000 TM that intends to address two problems with email: 1) help manage the inbox by automatically classifying email based on user folders, and 2) to aid in search and retrieval by providing a list of email relevant to the selected item. This add-in represents a first step in an experimental system for the study of other issues related to information management. The system has been set up to allow experimentation with other classification algorithms and the source code is available online in an effort to promote further experimentation.
Mining social networks for personalized email prioritization
Authors: Shinjae Yoo, Yiming Yang, Frank Lin, Il-Chul Moon
Venue: Conference KDD 2009
Year: 2009

Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization (PEP) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. This paper presents the first study (to the best of our knowledge) under such an assumption. Specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user. We also developed a novel semi-supervised (transductive) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network. These methods together enable us to obtain an enriched vector representation of each new email message, which consists of both standard features of an email message (such as words in the title or body, sender and receiver IDs, etc.) and the induced social features from the sender and receivers of the message. Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message, we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection. We obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection: the relative error reduction in MAE was 31% in micro-averaging, and 14% in macro-averaging.
Automatically summarising web sites: is there a way around it?
Authors: Einat Amitay, Cécile Paris
Venue: Conference CIKM 2000
Year: 2000

The challenge of automatically summarising Web pages and sites is a great one. However, currently there is no solution which offers an easy way to produce unbiased, coherent , and contentfull summaries of Web sites. In this work we suggest a new approach, which relies on the structure of hypertext and the way people describe information in it. As a proof-of-concept, we applied our approach to the problem of tailoring coherent snippets for search results. In this paper we describe the approach as it is implemented in our system, InCommonSense, and present results from a large scale evaluation of the snippets produced. We conclude by suggesting other applications that could make use of this technique.
Reducing the risk of query expansion via robust constrained optimization
Authors: Kevyn Collins-Thompson
Venue: Conference CIKM 2009
Year: 2009

We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. This framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. Our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. In addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. We show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. Our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. We provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.
Using anchor texts with their hyperlink structure for web search
Authors: Zhicheng Dou, Ruihua Song, Jian-Yun Nie, Ji-Rong Wen
Venue: Conference SIGIR 2009
Year: 2009

As a good complement to page content, anchor texts have been extensively used, and proven to be useful, in commercial search engines. However, anchor texts have been assumed to be independent, whether they come from the same Web site or not. Intuitively, an anchor text from unrelated Web sites should be considered as stronger evidence than that from the same site. This paper proposes two new methods to take into account the possible relationships between anchor texts. We consider two relationships in this paper: links from the same site and links from related sites. The importance assigned to the anchor texts in these two situations is discounted. Experimental results show that these two new models outperform the baseline model which assumes independence between hyperlinks.
Modeling anchor text and classifying queries to enhance web document retrieval
Authors: Atsushi Fujii
Venue: Conference WWW 2008
Year: 2008

Several types of queries are widely used on the World Wide Web and the expected retrieval method can vary depending on the query type. We propose a method for classifying queries into informational and navigational types. Because terms in navigational queries often appear in anchor text for links to other pages, we analyze the distribution of query terms in anchor texts on the Web for query classification purposes. While content-based retrieval is effective for informational queries, anchor-based retrieval is effective for navigational queries. Our retrieval system combines the results obtained with the content-based and anchor-based retrieval methods, in which the weight for each retrieval result is determined automatically depending on the result of the query classification. We also propose a method for improving anchor-based retrieval. Our retrieval method, which computes the probability that a document is retrieved in response to the given query, identifies synonyms of query terms in the anchor texts on the Web and uses these synonyms for smoothing purposes in the probability estimation. We use the NTCIR test collections and show the effectiveness of individual methods and the entire Web retrieval system experimentally.
IR evaluation methods for retrieving highly relevant documents
Authors: Kalervo Järvelin, Jaana Kekäläinen
Venue: Conference SIGIR 2000
Year: 2000

This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modern large IR environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (In-Query1) in a text database consisting of newspaper articles. The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents. The differences between the query types are practically essential and statistically significant. More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods.
Building enriched document representations using aggregated anchor text
Authors: Donald Metzler, Jasmine Novak, Hang Cui, Srihari Reddy
Venue: Conference SIGIR 2009
Year: 2009

It is well known that anchor text plays a critical role in a variety of search tasks performed over hypertextual domains, including enterprise search, wiki search, and web search. It is common practice to enrich a document's standard textual representation with all of the anchor text associated with its incoming hyperlinks. However, this approach does not help match relevant pages with very few inlinks. In this paper, we propose a method for overcoming anchor text sparsity by enriching document representations with anchor text that has been aggregated across the hyperlink graph. This aggregation mechanism acts to smooth, or diffuse, anchor text within a domain. We rigorously evaluate our proposed approach on a large web search test collection. Our results show the approach significantly improves retrieval effectiveness, especially for longer, more difficult queries.
Portfolio theory of information retrieval
Authors: Jun Wang, Jianhan Zhu
Venue: Conference SIGIR 2009
Year: 2009

This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not just about picking individual relevant documents, but about choosing the right combination of relevant documents. This motivates us to quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that balancing the overall relevance (mean) of the ranked list against its risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It generalizes the well-known probability ranking principle (PRP) by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Moreover, the benefit of diversification is mathematically quantified; we show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results in text retrieval confirm performance.
Risky business: modeling and exploiting uncertainty in information retrieval
Authors: Jianhan Zhu, Jun Wang, Ingemar J. Cox, Michael J. Taylor
Venue: Conference SIGIR 2009
Year: 2009

Most retrieval models estimate the relevance of each document to a query and rank the documents accordingly. However, such an approach ignores the uncertainty associated with the estimates of relevancy. If a high estimate of relevancy also has a high uncertainty, then the document may be very relevant or not relevant at all. Another document may have a slightly lower estimate of relevancy but the corresponding uncertainty may be much less. In such a circumstance, should the retrieval engine risk ranking the first document highest, or should it choose a more conservative (safer) strategy that gives preference to the second document? There is no definitive answer to this question, as it depends on the risk preferences of the user and the information retrieval system. In this paper we present a general framework for modeling uncertainty and introduce an asymmetric loss function with a single parameter that can model the level of risk the system is willing to accept. By adjusting the risk preference parameter, our approach can effectively adapt to users' different retrieval strategies.
Learning to predict where humans look
Authors: Tilke Judd, Krista A. Ehinger, Frédo Durand, Antonio Torralba 0001
Venue: Conference ICCV 2009
Year: 2009

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations. Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features. This large database of eye tracking data is publicly available with this paper.
Predicting the uncertainty of sentiment adjectives in indirect answers
Authors: Mitra Mohtarami, Hadi Amiri, Man Lan, Chew Lim Tan
Venue: Conference CIKM 2011
Year: 2011

Opinion question answering (QA) requires automatic and correct interpretation of an answer relative to its question. However, the ambiguity that often exists in the question-answer pairs causes complexity in interpreting the answers. This paper aims to infer yes/no answers from indirect yes/no question-answer pairs (IQAPs) that are ambiguous due to the presence of ambiguous sentiment adjectives. We propose a method to measure the uncertainty of the answer in an IQAP relative to its question. In particular, to infer the yes or no response from an IQAP, our method employs antonyms, synonyms, word sense disambiguation as well as the semantic association between the sentiment adjectives that appear in the IQAP. Extensive experiments demonstrate the effectiveness of our method over the baseline.
YAGO2: Exploring and querying world knowledge in time, space, context, and many languages
Authors: Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Edwin Lewis-Kelham, Gerard de Melo, Gerhard Weikum
Venue: Conference WWW 2011
Year: 2011

We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as first-class citizens. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.
A software framework for matchmaking based on semantic web technology
Authors: Lei Li, Ian Horrocks
Venue: Conference WWW 2003
Year: 2003

An important objective of the Semantic Web is to make Electronic Commerce interactions more flexible and automated. To achieve this, standardization of ontologies, message content and message protocols will be necessary.In this paper we investigate how Semantic and Web Services technologies can be used to support service advertisement and discovery in e-commerce. In particular, we describe the design and implementation of a service matchmaking prototype which uses a DAML-S based ontology and a Description Logic reasoner to compare ontology based service descriptions. We also present the results of initial experiments testing the performance of this prototype implementation in a realistic agent based e-commerce scenario.
Nonnegative shared subspace learning and its application to social media retrieval
Authors: Sunil Kumar Gupta, Dinh Q. Phung, Brett Adams, Truyen Tran, Svetha Venkatesh
Venue: Conference KDD 2010
Year: 2010

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.
A cross-collection mixture model for comparative text mining
Authors: ChengXiang Zhai, Atulya Velivelli, Bei Yu
Venue: Conference KDD 2004
Year: 2004

In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.
A self-training approach for resolving object coreference on the semantic web
Authors: Wei Hu, Jianfeng Chen, Yuzhong Qu
Venue: Conference WWW 2011
Year: 2011

An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify "equivalent" URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs statements, but potentially coreferent ones are still considerable. Existing approaches address the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which finds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between property-value pairs, which is not always accurate enough. In this paper, we propose a self-training approach for object coreference resolution on the Semantic Web, which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates. For an object URI, we firstly establish a kernel that consists of semantically coreferent URIs based on owl:sameAs, (inverse) functional properties and (max-)cardinalities, and then extend such kernel iteratively in terms of discriminative property-value pairs in the descriptions of URIs. In particular, the discriminability is learnt with a statistical measurement, which not only exploits key characteristics for representing an object, but also takes into account the matchability between properties from pragmatics. In addition, frequent property combinations are mined to improve the accuracy of the resolution. We implement a scalable system and demonstrate that our approach achieves good precision and recall for resolving object coreference, on both benchmark and large-scale datasets.