OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive context features for toponym resolution in streaming news
Authors: Michael D. Lieberman, Hanan Samet
Venue: Conference SIGIR 2012
Year: 2012

News sources around the world generate constant streams of information, but effective streaming news retrieval requires an intimate understanding of the geographic content of news. This process of understanding, known as geotagging, consists of first finding words in article text that correspond to location names (toponyms), and second, assigning each toponym its correct lat/long values. The latter step, called toponym resolution, can also be considered a classification problem, where each of the possible interpretations for each toponym is classified as correct or incorrect. Hence, techniques from supervised machine learning can be applied to improve accuracy. New classification features to improve toponym resolution, termed adaptive context features, are introduced that consider a window of context around each toponym, and use geographic attributes of toponyms in the window to aid in their correct resolution. Adaptive parameters controlling the window's breadth and depth afford flexibility in managing a tradeoff between feature computation speed and resolution accuracy, allowing the features to potentially apply to a variety of textual domains. Extensive experiments with three large datasets of streaming news demonstrate the new features' effectiveness over two widely-used competing methods.
A cross-modal study of pain across communities in the united states
Authors: Arnav Aggarwal, Sunny Rai, Salvatore Giorgi, Shreya Havaldar, Garrick Sherman, Juhi Mittal, Sharath Chandra Guntuku
Venue: Conference WWW 2023
Year: 2023

Pain is one of the most prevalent reasons for seeking medical attention in the United States. Understanding how different communities report and express pain can aid in directing medical efforts and in advancing precision pain management. Using a large-scale self-report survey data set on pain from Gallup (2.5 million surveys) and social media posts from Twitter (1.8 million tweets), we investigate a) if Twitter posts could predict community-level pain and b) how expressions of pain differ across communities in the United States. Beyond observing an improvement of over 9% (in Pearson r) when using Twitter language over demographics to predict community-level pain, our study reveals that the discourse on pain varied significantly across communities in the United States. Evangelical Hubs frequently post about God, lessons from struggle, and prayers when expressing pain, whereas Working Class Country posts about regret and extreme endurance. Academic stresses, injuries, painkillers, and surgeries were the most commonly discussed pain themes in College Towns; Graying America discussed therapy, used emotional language around empathy and anger, and posted about chronic pain treatment; the African American South posted about struggles, patience, and faith when talking about pain. Our study demonstrates the efficacy of using Twitter to predict survey-based self-reports of pain across communities and has implications in aiding community-focused pain management interventions.
Pintext: A multitask text embedding system in pinterest
Authors: Jinfeng Zhuang, Yu Liu
Venue: Conference KDD 2019
Year: 2019

Text embedding is a fundamental component for extracting text features in production-level data mining and machine learning systems given textual information is the most ubiqutious signals. However, practitioners often face the tradeoff between effectiveness of underlying embedding algorithms and cost of training and maintaining various embedding results in large-scale applications. In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. Specifically, we learn word level semantic vectors by enforcing that the similarity between positive engagement pairs is larger than the similarity between a randomly sampled background pairs. Based on the learned semantic vectors, we derive embedding vector of a user, a pin, or a search query by simply averaging its word level vectors. In this common compact vector space, we are able to do unified nearest neighbor search with hashing by Hadoop jobs or dockerized images on Kubernetes cluster. Both offline evaluation and online experiments show effectiveness of this PinText system and save storage cost of multiple open-sourced embeddings significantly.
Investigate transitions into drug addiction through text mining of reddit data
Authors: John Lu, Sumati Sridhar, Ritika Pandey, Mohammad Al Hasan, George O. Mohler
Venue: Conference KDD 2019
Year: 2019

Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtained data from Reddit, an online collection of forums, to gather insight into drug use/misuse using text snippets from users narratives. Specifically, using users' posts, we trained a binary classifier which predicts a user's transitions from casual drug discussion forums to drug recovery forums. We also proposed a Cox regression model that outputs likelihoods of such transitions. In doing so, we found that utterances of select drugs and certain linguistic features contained in one's posts can help predict these transitions. Using unfiltered drug-related posts, our research delineates drugs that are associated with higher rates of transitions from recreational drug discussion to support/recovery discussion, offers insight into modern drug culture, and provides tools with potential applications in combating the opioid crisis.
On answer position bias in transformers for question answering
Authors: Rafael Glater, Rodrygo L. T. Santos
Venue: Conference SIGIR 2023
Year: 2023

Extractive Transformer-based models for question answering (QA) are trained to predict the start and end position of the answer in a candidate paragraph. However, the true answer position can bias these models when its distribution in the training data is highly skewed. That is, models trained only with the answer at the beginning of the paragraph will perform poorly on test instances with the answer at the end. Many studies have focused on countering answer position bias but have yet to deepen our understanding of how such bias manifests in the main components of the Transformer. In this paper, we analyze the self-attention and embedding generation components of five Transformer-based models with different architectures and position embedding strategies. Our analysis shows that models tend to map position bias in their attention matrices, generating embeddings that correlate the answer and its biased position, ultimately compromising model generalization.
The impact of more transparent interfaces on behavior in personalized recommendation
Authors: Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims
Venue: Conference SIGIR 2020
Year: 2020

Many interactive online systems, such as social media platforms or news sites, provide personalized experiences through recommendations or news feed customization based on people's feedback and engagement on individual items (e.g., liking items). In this paper, we investigate how we can support a greater degree of user control in such systems by changing the way the system allows people to gauge the consequences of their feedback actions. To this end, we consider two important aspects of how the system responds to feedback actions: (i) immediacy, i.e., how quickly the system responds with an update, and (ii) visibility, i.e., whether or not changes will get highlighted. We used both an in-lab qualitative study and a large-scale crowd-sourced study to examine the impact of these factors on people's reported preferences and observed behavioral metrics. We demonstrate that UX design which enables people to preview the impact of their actions and highlights changes results in a higher reported transparency, an overall preference for this design, and a greater selectivity in which items are liked.
Sign language recognition using sequential pattern trees
Authors: Eng-Jon Ong, Helen Cooper, Nicolas Pugeault, Richard Bowden
Venue: Conference CVPR 2012
Year: 2012

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.
Stfu noob! predicting crowdsourced decisions on toxic behavior in online games
Authors: Jeremy Blackburn, Haewoon Kwak
Venue: Conference WWW 2014
Year: 2014

One problem facing players of competitive games is negative, or toxic, behavior. League of Legends, the largest eSport game, uses a crowdsourcing platform called the Tribunal to judge whether a reported toxic player should be punished or not. The Tribunal is a two stage system requiring reports from those players that directly observe toxic behavior, and human experts that review aggregated reports. While this system has successfully dealt with the vague nature of toxic behavior by majority rules based on many votes, it naturally requires tremendous cost, time, and human efforts. In this paper, we propose a supervised learning approach for predicting crowdsourced decisions on toxic behavior with large-scale labeled data collections; over 10 million user reports involved in 1.46 million toxic players and corresponding crowdsourced decisions. Our result shows good performance in detecting overwhelmingly majority cases and predicting crowdsourced decisions on them. We demonstrate good portability of our classifier across regions. Finally, we estimate the practical implications of our approach, potential cost savings and victim protection.
Dimension importance estimation for dense information retrieval
Authors: Guglielmo Faggioli, Nicola Ferro, Raffaele Perego, Nicola Tonellotto
Venue: Conference SIGIR 2024
Year: 2024

Recent advances in Information Retrieval have shown the effectiveness of embedding queries and documents in a latent high-dimensional space to compute their similarity. While operating on such high-dimensional spaces is effective, in this paper, we hypothesize that we can improve the retrieval performance by adequately moving to a query-dependent subspace. More in detail, we formulate the Manifold Clustering (MC) Hypothesis: projecting queries and documents onto a subspace of the original representation space can improve retrieval effectiveness. To empirically validate our hypothesis, we define a novel class of Dimension IMportance Estimators (DIME). Such models aim to determine how much each dimension of a high-dimensional representation contributes to the quality of the final ranking and provide an empirical method to select a subset of dimensions where to project the query and the documents. To support our hypothesis, we propose an oracle DIME, capable of effectively selecting dimensions and almost doubling the retrieval performance. To show the practical applicability of our approach, we then propose a set of DIMEs that do not require any oracular piece of information to estimate the importance of dimensions. These estimators allow us to carry out a dimensionality selection that enables performance improvements of up to +11.5% (moving from 0.675 to 0.752 nDCG@10) compared to the baseline methods using all dimensions. Finally, we show that, with simple and realistic active feedback, such as the user's interaction with a single relevant document, we can design a highly effective DIME, allowing us to outperform the baseline by up to +0.224 nDCG@10 points (+58.6%, moving from 0.384 to 0.608).
Relational matching with dynamic graph structures
Authors: Richard C. Wilson 0001, Edwin R. Hancock
Venue: Conference ICCV 1995
Year: 1995

The paper describes a novel approach to relational matching problems in machine vision. Rather than matching static scene descriptions, the approach adopts an active representation of the data to be matched. This representation is iteratively reconfigured to increase its degree of topological congruency with the model relational structure in a reconstructive matching process. The active reconfiguration of relational structures is controlled by a MAP update process. The final restored graph representation is optimal in the sense that it has maximum a posteriori probability with respect to the available attributes for the objects under match. The benefits of the technique are demonstrated experimentally on the matching of cluttered synthetic aperture radar data to a model in the form of a digital map. The operational limits of the method are established in a simulation study. >
A General Framework for Object Detection
Authors: Constantine Papageorgiou, Michael Oren, Tomaso A. Poggio
Venue: Conference ICCV 1998
Year: 1998

This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.
Learning non-negative sparse image codes by convex programming
Authors: Matthias Heiler, Christoph Schnörr
Venue: Conference ICCV 2005
Year: 2005

Example-based learning of codes that statistically encode general image classes is of vital importance for computational vision. Recently non negative matrix factorization (NMF) was suggested to provide image code that was both sparse and localized, in contrast to established non local methods like PCA. In this paper, we adopt and generalize this approach to develop a novel learning framework that allows to efficiently compute sparsity-controlled invariant image codes by a well defined sequence of convex conic programs. Applying the corresponding parameter-free algorithm to various image classes results in semantically relevant and transformation-invariant image representations that are remarkably robust against noise and quantization
Object detection and segmentation from joint embedding of parts and pixels
Authors: Michael Maire, Stella X. Yu, Pietro Perona
Venue: Conference ICCV 2011
Year: 2011

We present a new framework in which image segmentation, figure/ground organization, and object detection all appear as the result of solving a single grouping problem. This framework serves as a perceptual organization stage that integrates information from low-level image cues with that of high-level part detectors. Pixels and parts each appear as nodes in a graph whose edges encode both affinity and ordering relationships. We derive a generalized eigen-problem from this graph and read off an interpretation of the image from the solution eigenvectors. Combining an off-the-shelf top-down part-based person detector with our low-level cues and grouping formulation, we demonstrate improvements to object detection and segmentation.
Proposal generation for object detection using cascaded ranking svms
Authors: Ziming Zhang, Jonathan Warrell, Philip H. S. Torr
Venue: Conference CVPR 2011
Year: 2011

Object recognition has made great strides recently. However, the best methods, such as those based on kernel-SVMs are highly computationally intensive. The problem of how to accelerate the evaluation process without decreasing accuracy is thus of current interest. In this paper, we deal with this problem by using the idea of ranking. We propose a cascaded architecture which using the ranking SVM generates an ordered set of proposals for windows containing object instances. The top ranking windows may then be fed to a more complex detector. Our experiments demonstrate that our approach is robust, achieving higher overlap-recall values using fewer output proposals than the state-of-the-art. Our use of simple gradient features and linear convolution indicates that our method is also faster than the state-of-the-art.
Multi-modality disease modeling via collective deep matrix factorization
Authors: Qi Wang, Mengying Sun, Liang Zhan, Paul Thompson, Shuiwang Ji, Jiayu Zhou
Venue: Conference KDD 2017
Year: 2017

Alzheimer's disease (AD), one of the most common causes of dementia, is a severe irreversible neurodegenerative disease that results in loss of mental functions. The transitional stage between the expected cognitive decline of normal aging and AD, mild cognitive impairment (MCI), has been widely regarded as a suitable time for possible therapeutic intervention. The challenging task of MCI detection is therefore of great clinical importance, where the key is to effectively fuse predictive information from multiple heterogeneous data sources collected from the patients. In this paper, we propose a framework to fuse multiple data modalities for predictive modeling using deep matrix factorization, which explores the non-linear interactions among the modalities and exploits such interactions to transfer knowledge and enable high performance prediction. Specifically, the proposed collective deep matrix factorization decomposes all modalities simultaneously to capture non-linear structures of the modalities in a supervised manner, and learns a modality specific component for each modality and a modality invariant component across all modalities. The modality invariant component serves as a compact feature representation of patients that has high predictive power. The modality specific components provide an effective means to explore imaging genetics, yielding insights into how imaging and genotype interact with each other non-linearly in the AD pathology. Extensive empirical studies using various data modalities provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method for fusing heterogeneous modalities.
Learning interpretable metric between graphs: Convex formulation and computation with graph mining
Authors: Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
Venue: Conference KDD 2019
Year: 2019

Graph is a standard approach to modeling structured data. Although many machine learning methods depend on the metric of the input objects, defining an appropriate distance function on graph is still a controversial issue. We propose a novel supervised metric learning method for a subgraph-based distance, called interpretable graph metric learning (IGML). IGML optimizes the distance function in such a way that a small number of important subgraphs can be adaptively selected. This optimization is computationally intractable with naive application of existing optimization algorithms. We construct a graph mining based efficient algorithm to deal with this computational difficulty. Important advantages of our method are 1) guarantee of the optimality from the convex formulation, and 2) high interpretability of results. To our knowledge, none of the existing studies provide an interpretable subgraph-based metric in a supervised manner. In our experiments, we empirically verify superior or comparable prediction performance of IGML to other existing graph classification methods which do not have clear interpretability. Further, we demonstrate usefulness of IGML through some illustrative examples of extracted subgraphs and an example of data analysis on the learned metric space.
Statistical mechanics methods for discovering knowledge from modern production quality neural networks
Authors: Charles H. Martin, Michael W. Mahoney
Venue: Conference KDD 2019
Year: 2019

There have long been connections between statistical mechanics and neural networks, but in recent decades these connections have withered. However, in light of recent failings of statistical learning theory and stochastic optimization theory to describe, even qualitatively, many properties of production-quality neural network models, researchers have revisited ideas from the statistical mechanics of neural networks. This tutorial will provide an overview of the area; it will go into detail on how connections with random matrix theory and heavy-tailed random matrix theory can lead to a practical phenomenological theory for large-scale deep neural networks; and it will describe future directions.
Robust tracking via weakly supervised ranking SVM
Authors: Yancheng Bai, Ming Tang 0001
Venue: Conference CVPR 2012
Year: 2012

Appearance model is a key component of tracking algorithms. Most existing approaches utilize the object information contained in the current and previous frames to construct the object appearance model and locate the object with the model in frame t + 1. This method may work well if the object appearance just fluctuates in short time intervals. Nevertheless, suboptimal locations will be generated in frame t + 1 if the visual appearance changes substantially from the model. Then, continuous changes would accumulate errors and finally result in a tracking failure. To copy with this problem, in this paper we propose a novel algorithm - online Laplacian ranking support vector tracker (LRSVT) - to robustly locate the object. The LRSVT incorporates the labeled information of the object in the initial and the latest frames to resist the occlusion and adapt to the fluctuation of the visual appearance, and the weakly labeled information from frame t + 1 to adapt to substantial changes of the appearance. Extensive experiments on public benchmark sequences show the superior performance of LRSVT over some state-of-the-art tracking algorithms.
Unsupervised modeling of object categories using link analysis techniques
Authors: Gunhee Kim, Christos Faloutsos, Martial Hebert
Venue: Conference CVPR 2008
Year: 2008

We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interactions of all unit visual features across the entire training set and we infer information, such as which features are in which categories, directly from the graph by using link analysis techniques. The link analysis techniques are based on well-established graph mining techniques used in diverse applications such as WWW, bioinformatics, and social networks. The techniques operate directly on the patterns of connections between features in the graph rather than on statistical properties, e.g., from clustering in feature space. We argue that the resulting techniques are simpler, and we show that they perform similarly or better compared to state of the art techniques on common data sets. We also show results on more challenging data sets than those that have been used in prior work on unsupervised modeling.