OpenRaiser/Intern-Atlas huggingface dataset:
ILDA: interdependent lda model for learning latent aspects and their ratings from online product reviews
Authors: Samaneh Moghaddam, Martin Ester
Venue: Conference SIGIR 2011
Year: 2011

Today, more and more product reviews become available on the Internet, e.g., product review forums, discussion groups, and Blogs. However, it is almost impossible for a customer to read all of the different and possibly even contradictory opinions and make an informed decision. Therefore, mining online reviews (opinion mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in opinion mining. An aspect is an attribute or component of a product, e.g. 'screen' for a digital camera. It is common that reviewers use different words to describe an aspect (e.g. 'LCD', 'display', 'screen'). A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'blurry screen'. In this paper we present three probabilistic graphical models which aim to extract aspects and corresponding ratings of products from online reviews. The first two models extend standard PLSI and LDA to generate a rated aspect summary of product reviews. As our main contribution, we introduce Interdependent Latent Dirichlet Allocation (ILDA) model. This model is more natural for our task since the underlying probabilistic assumptions (interdependency between aspects and ratings) are appropriate for our problem domain. We conduct experiments on a real life dataset, Epinions.com, demonstrating the improved effectiveness of the ILDA model in terms of the likelihood of a held-out test set, and the accuracy of aspects and aspect ratings.
Multi-aspect opinion polling from textual reviews
Authors: Jingbo Zhu, Huizhen Wang, Benjamin K. Tsou, Muhua Zhu
Venue: Conference CIKM 2009
Year: 2009

This paper presents an unsupervised approach to aspect-based opinion polling from raw textual reviews without explicit ratings. The key contribution of this paper is three-fold. First, a multi-aspect bootstrapping algorithm is proposed to learn from unlabeled data aspect-related terms of each aspect to be used for aspect identification. Second, an unsupervised segmentation model is proposed to address the challenge of identifying multiple single-aspect units in a multi-aspect sentence. Finally, an aspect-based opinion polling algorithm is presented. Experiments on real Chinese restaurant reviews show that our opinion polling method can achieve 75.5% precision performance.
Detecting unusual activity in video
Authors: Hua Zhong, Jianbo Shi, Mirkó Visontai
Venue: Conference CVPR 2004
Year: 2004

We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.
Sub-event detection during natural hazards using features of social media data
Authors: Dhekar Abhik, Durga Toshniwal
Venue: Conference WWW 2013
Year: 2013

Social networking sites such as Flickr, YouTube, Facebook, etc. contain a huge amount of user-contributed data for a variety of real-world events. These events can be some natural calamities such as earthquakes, floods, forest fires, etc. or some man-made hazards like riots. This work focuses on getting better knowledge about a natural hazard event using the data available from social networking sites. Rescue and relief activities in emergency situations can be enhanced by identifying sub-events of a particular event. Traditional topic discovery techniques used for event identification in news data cannot be used for social media data because social network data may be unstructured. To address this problem the features or metadata associated with social media data can be exploited. These features can be user-provided annotations (e.g., title, description) and automatically generated information (e.g., content creation time). Considerable improvement in performance is observed by using multiple features of social media data for sub-event detection rather than using individual feature. Proposed here is a two-step process. In the first step, clusters are formed from social network data using relevant features individually. Based on the significance of features weights are assigned to them. And in the second step all the clustering solutions formed in the first step are combined in a principal weighted manner to give the final clustering solution. Each cluster represents a sub-event for a particular natural hazard.
Automatic sub-event detection in emergency management using social media
Authors: Daniela Pohl, Abdelhamid Bouchachia, Hermann Hellwagner
Venue: Conference WWW 2012
Year: 2012

Emergency management is about assessing critical situations, followed by decision making as a key step. Clearly, information is crucial in this two-step process. The technology of social (multi)media turns out to be an interesting source for collecting information about an emergency situation. In particular, situational information can be captured in form of pictures, videos, or text messages. The present paper investigates the application of multimedia metadata to identify the set of sub-events related to an emergency situation. The used metadata is compiled from Flickr and YouTube during an emergency situation, where the identification of the events relies on clustering. Initial results presented in this paper show how social media data can be used to detect different sub-events in a critical situation.
Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs
Authors: Kazufumi Watanabe, Masanao Ochi, Makoto Okabe, Rikio Onai
Venue: Conference CIKM 2011
Year: 2011

We propose a system for detecting local events in the real-world using geolocation information from microblog documents. A local event happens when people with a common purpose gather at the same time and place. To detect such an event, we identify a group of Twitter documents describing the same theme that were generated within a short time and a small geographic area. Timestamps and geotags are useful for finding such documents, but only 0.7% of documents are geotagged and not sufficient for this purpose. Therefore, we propose an automatic geotagging method that identifies the location of non-geotagged documents. Our geotagging method successfully increased the number of geographic groups by about 115 times. For each group of documents, we extract co-occurring terms to identify its theme and determine whether it is about an event. We subjectively evaluated the precision of our detected local events and found that it had 25.5% accuracy. These results demonstrate that our system can detect local events that are difficult to identify using existing event detection methods. A user can interactively specify the size of a desired event by manipulating the parameters of date, area size, and the minimum number of Twitter users associated with the location. Our system allows users to enjoy the novel experience of finding a local event happening near their current location in real time.
Socialtransfer: Transferring social knowledge for cold-start cowdsourcing
Authors: Zhou Zhao, James Cheng, Furu Wei, Ming Zhou, Wilfred Ng, Yingjun Wu
Venue: Conference CIKM 2014
Year: 2014

An essential component of building a successful crowdsourcing market is effective task matching, which matches a given task to the right crowdworkers. In order to provide high- quality task matching, crowdsourcing systems rely on past task-solving activities of crowdworkers. However, the average number of past activities of crowdworkers in most crowd- sourcing systems is very small. We call the workers who have only solved a small number of tasks cold-start crowdworkers. We observe that most of the workers in crowdsourcing systems are cold-start crowdworkers, and crowdsourcing systems actually enjoy great benefits from cold-start crowd-workers. However, the problem of task matching with the presence of many cold-start crowdworkers has not been well studied. We propose a new approach to address this issue. Our main idea, motivated by the prevalence of online social networks, is to transfer the knowledge about crowdworkers in their social networks to crowdsourcing systems for task matching. We propose a SocialTransfer model for cold-start crowdsourcing, which not only infers the expertise of warm- start crowdworkers from their past activities, but also transfers the expertise knowledge to cold-start crowdworkers via social connections. We evaluate the SocialTransfer model on the well-known crowdsourcing system Quora, using knowledge from the popular social network Twitter. Experimental results show that, by transferring social knowledge, our method achieves significant improvements over the state-of-the-art methods.
Manifold-manifold distance with application to face recognition based on image set
Authors: Ruiping Wang 0001, Shiguang Shan, Xilin Chen 0001, Wen Gao 0001
Venue: Conference CVPR 2008
Year: 2008

In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. The proposed MMD method is evaluated on the task of Face Recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.
Tweet-recommender: Finding relevant tweets for news articles
Authors: Ralf Krestel, Thomas Werkmeister, Timur Pratama Wiradarma, Gjergji Kasneci
Venue: Conference WWW 2015
Year: 2015

Twitter has become a prime source for disseminating news and opinions. However, the length of tweets prohibits detailed descriptions; instead, tweets sometimes contain URLs that link to detailed news articles. In this paper, we devise generic techniques for recommending tweets for any given news article. To evaluate and compare the different techniques, we collected tens of thousands of tweets and news articles and conducted a user study on the relevance of recommendations.
Adapting the right measures for k-means clustering
Authors: Junjie Wu, Hui Xiong, Jian Chen
Venue: Conference KDD 2009
Year: 2009

Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the performance of clustering algorithms, these measures often provide inconsistent information about the clustering performance and the best suitable measures to use in practice remain unknown. This paper thus fills this crucial void by giving an organized study of 16 external validation measures for K-means clustering. Specifically, we first introduce the importance of measure normalization in the evaluation of the clustering performance on data with imbalanced class distributions. We also provide normalization solutions for several measures. In addition, we summarize the major properties of these external measures. These properties can serve as the guidance for the selection of validation measures in different application scenarios. Finally, we reveal the interrelationships among these external measures. By mathematical transformation, we show that some validation measures are equivalent. Also, some measures have consistent validation performances. Most importantly, we provide a guide line to select the most suitable validation measures for K-means clustering.
Extracting sentence segments for text summarization: a machine learning approach
Authors: Wesley T. Chuang, Jihoon Yang
Venue: Conference SIGIR 2000
Year: 2000

With the proliferation of the Internet and the huge amount of data it transfers, text summarization is becoming more important. We present an approach to the design of an automatic text summarizer that generates a summary by extracting sentence segments. First, sentences are broken into segments by special cue markers. Each segment is represented by a set of predefined features (e.g. location of the segment, average term frequencies of the words occurring in the segment, number of title words in the segment, and the like). Then a supervised learning algorithm is used to train the summarizer to extract important sentence segments, based on the feature vector. Results of experiments on U.S. patents indicate that the performance of the proposed approach compares very favorably with other approaches (including Microsoft Word summarizer) in terms of precision, recall, and classification accuracy.
A case study of behavior-driven conjoint analysis on yahoo! front page today module
Authors: Wei Chu, Seung-Taek Park, Todd Beaupre, Nitin Motgi, Amit Phadke, Seinjuti Chakraborty, Joe Zachariah
Venue: Conference KDD 2009
Year: 2009

Conjoint analysis is one of the most popular market research methodologies for assessing how customers with heterogeneous preferences appraise various objective characteristics in products or services, which provides critical inputs for many marketing decisions, e.g. optimal design of new products and target market selection. Nowadays it becomes practical in e-commercial applications to collect millions of samples quickly. However, the large-scale data sets make traditional conjoint analysis coupled with sophisticated Monte Carlo simulation for parameter estimation computationally prohibitive. In this paper, we report a successful large-scale case study of conjoint analysis on click through stream in a real-world application at Yahoo!. We consider identifying users' heterogenous preferences from millions of click/view events and building predictive models to classify new users into segments of distinct behavior pattern. A scalable conjoint analysis technique, known as tensor segmentation, is developed by utilizing logistic tensor regression in standard partworth framework for solutions. In offline analysis on the samples collected from a random bucket of Yahoo! Front Page Today Module, we compare tensor segmentation against other segmentation schemes using demographic information, and study user preferences on article content within tensor segments. Our knowledge acquired in the segmentation results also provides assistance to editors in content management and user targeting. The usefulness of our approach is further verified by the observations in a bucket test launched in Dec. 2008.
Effects of task and domain on searcher attention
Authors: Dmitry Lagun, Eugene Agichtein
Venue: Conference SIGIR 2014
Year: 2014

Previous studies of online user attention during information seeking tasks have mainly focused on analyzing searcher behavior in the web search settings. While these studies enabled better understanding of search result examination, their findings might not generalize for the tasks and search interfaces in other domains such as Shopping or Social Media. In this paper we present, to best of our knowledge, the first cross-domain comparison of search examination behavior and patterns of aggregated attention across Web Search, News, Shopping and Social Network domains. We investigate how domain of the search and the scope of the information need affect search examination, and find significant differences beyond those arising from natural disparities between individuals. For example, we find that the mean fixation duration, a common indicator of cognitive load, varies significantly across domains (e.g., mean fixation duration in the Social Network domain exceeds that of general Web Search by over 30%). We also find large differences in the aggregate patterns of user attention on the screen, especially in the Shopping and Social Network domains compared to the Web Search domain, emphasizing the need for domain specific user models and evaluation metrics.
Joint learning and dictionary construction for pattern recognition
Authors: Duc-Son Pham 0001, Svetha Venkatesh
Venue: Conference CVPR 2008
Year: 2008

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.
Classification and Clustering via dictionary learning with structured incoherence and shared features
Authors: Ignacio Ramírez, Pablo Sprechmann, Guillermo Sapiro
Venue: Conference CVPR 2010
Year: 2010

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as a union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. An incoherence promoting term encourages dictionaries associated to different classes to be as independent as possible, while still allowing for different classes to share features. This term directly acts on the dictionaries, thereby being applicable both in the supervised and unsupervised settings. Using learned dictionaries for classification and clustering makes this method robust and well suited to handle large datasets. The proposed framework uses a novel measurement for the quality of the sparse representation, inspired by the robustness of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization term in sparse coding. In the case of unsupervised classification and/or clustering, a new initialization based on combining sparse coding with spectral clustering is proposed. This initialization clusters the dictionary atoms, and therefore is based on solving a low dimensional eigen-decomposition problem, being applicable to large datasets. We first illustrate the proposed framework with examples on standard image and speech datasets in the supervised classification setting, obtaining results comparable to the state-of-the-art with this simple approach. We then present experiments for fully unsupervised clustering on extended standard datasets and texture images, obtaining excellent performance.
Learning inter-related visual dictionary for object recognition
Authors: Ning Zhou, Yi Shen 0005, Jinye Peng 0001, Jianping Fan 0001
Venue: Conference CVPR 2012
Year: 2012

Object recognition is challenging especially when the objects from different categories are visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) algorithm to exploit the visual correlation within a group of visually similar object categories for dictionary learning where a commonly shared dictionary and multiple category-specific dictionaries are accordingly modeled. To enhance the discrimination of the dictionaries, the dictionary learning problem is formulated as a joint optimization by adding a discriminative term on the principle of the Fisher discrimination criterion. As well as presenting the JDL model, a classification scheme is developed to better take advantage of the multiple dictionaries that have been trained. The effectiveness of the proposed algorithm has been evaluated on popular visual benchmarks.
Moving object detection on a runway prior to landing using an onboard infrared camera
Authors: Cheng-Hua Pai, Yuping Lin, Gérard G. Medioni, Ray Rida Hamza
Venue: Conference CVPR 2007
Year: 2007

Determining the status of a runway prior to landing is essential for any aircraft, whether manned or unmanned. In this paper, we present a method that can detect moving objects on the runway from an onboard infrared camera prior to the landing phase. Since the runway is a planar surface, we first locally stabilize the sequence to automatically selected reference frames using feature points in the neighborhood of the runway. Next, we normalize the stabilized sequence to compensate for the global intensity variation caused by the gain control of the infrared camera. We then create a background model to learn an appearance model of the runway. Finally, we identify moving objects by comparing the image sequence with the background model. We have tested our system with both synthetic and real world data and show that it can detect distant moving objects on the runway. We also provide a quantitative analysis of the performance with respect to variations in size, direction and speed of the target.