OpenRaiser/Intern-Atlas huggingface dataset:
Latent aspect detection via backtranslation augmentation
Authors: Farinam Hemmatizadeh, Christine Wong, Alice Yu, Hossein Fani
Venue: Conference CIKM 2023
Year: 2023

Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers identify shortcomings and prioritize customers' needs. Existing methods focus on detecting the surface form of an aspect falling short when aspects are latent in reviews, especially in an informal context like in social posts. In this paper, we propose data augmentation via natural language backtranslation to extract latent occurrences of aspects. We presume that backtranslation (1) can reveal latent aspects because they may not be commonly known in the target language and can be generated through backtranslation; (2) augments context-aware synonymous aspects from a target language to the original language, hence addressing the out-of-vocabulary issue; and (3) helps with the semantic disambiguation of polysemous words and collocations. Through our experiments on well-known aspect detection methods across semeval datasets of restaurant and laptop reviews, we demonstrate that review augmentation via backtranslation yields a steady performance boost in baselines. We further contribute LADy at https://github.com/fani-lab/LADy, a benchmark library to support the reproducibility of our research.
Osprey: A reference framework for online grooming detection via neural models and conversation features
Authors: Hamed Waezi, Reza Barzegar, Hossein Fani
Venue: Conference CIKM 2024
Year: 2024

Online grooming is the process of an adult initiating a sexual relationship with a minor through online conversation platforms. While neural models are developed to detect such incidents, their practical implications in real-world settings remain moot for their closed, irreproducible, and poor evaluation methodologies under the sparse distribution of grooming conversations in the training datasets, like undermining recall over precision. Furthermore, proposed models overlook characteristic features of grooming in online conversations, including the number of participants, message exchange patterns, and temporal signals, such as the elapsed times between messages. In this paper, we foremost contribute Osprey, an open-source library to support a standard pipeline and experimental details, incorporating canonical neural models and a variety of vector representation learning for conversations while accommodating new models and training datasets. Further, we incorporate conversation features into the models to improve recall while maintaining precision. Our experiments across neural baselines and vector representations of conversations demonstrated that recurrent neural models, particularly gru, on the sequence of pretrained transformer-based embeddings of messages in a conversation along with conversation features obtain state-of-the-art performance, winning the best recall with competitive precision. Osprey is available at https://github.com/fani-lab/Osprey/tree/cikm24.
Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances
Authors: Puhai Yang, Heyan Huang, Wei Wei 0002, Xian-Ling Mao
Venue: Conference KDD 2022
Year: 2022

Recently, the research of dialogue systems has been widely concerned, especially task-oriented dialogue systems, which have received increased attention due to their wide application prospect. As a core component, dialogue state tracking (DST) plays a key role in task-oriented dialogue systems, and its function is to parse natural language dialogues into dialogue state formed by slot-value pairs. It is well known that dialogue state tracking has been well studied and explored on current benchmark datasets such as the MultiWOZ. However, almost all current research completely ignores the user negative feedback utterances that exist in real-life conversations when a system error occurs, which often contains user-provided corrective information for the system error. Obviously, user negative feedback utterances can be used to correct the inevitable errors in automatic speech recognition and model generalization. Thus, in this paper, we will explore the role of negative feedback utterances in dialogue state tracking in detail through simulated negative feedback utterances. Specifically, due to the lack of dataset involving negative feedback utterances, first, we have to define the schema of user negative feedback utterances and propose a joint modeling method for feedback utterance generation and filtering. Then, we explore three aspects of interaction mechanism that should be considered in real-life conversations involving negative feedback utterances and propose evaluation metrics related to negative feedback utterances. Finally, on WOZ2.0 and MultiWOZ2.1 datasets, by constructing simulated negative feedback utterances in training and testing, we not only verify the important role of negative feedback utterances in dialogue state tracking, but also analyze the advantages and disadvantages of different interaction mechanisms involving negative feedback utterances, lighting future research on negative feedback utterances.
A coherent unsupervised model for toponym resolution
Authors: Ehsan Kamalloo, Davood Rafiei
Venue: Conference WWW 2018
Year: 2018

Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content. However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task. In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data. We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing. Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution. Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques. We also examine three commercial products including Reuters OpenCalais, Yahoo! YQL Placemaker, and Google Cloud Natural Language API. The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.
Informed Multi-context Entity Alignment
Authors: Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Xiaofang Zhou
Venue: Conference WSDM 2022
Year: 2022

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.
An unsupervised detection framework for chinese jargons in the darknet
Authors: Liang Ke, Xinyu Chen, Haizhou Wang
Venue: Conference WSDM 2022
Year: 2022

With the continuous development of the darknet technology, the scale of darknet and have increased rapidly in recent years, leading to rampant crime in these anonymous trading markets. Monitoring these markets can effectively combat the criminal forces that hide behind them. One of the difficulties in understanding the darknet is that criminals usually use jargons to disguise transactions and thus avoid surveillance. These jargons usually distort the original meaning of innocent-looking words in obscure ways, posing significant challenges for crime tracking. Current research on Chinese jargon detection mainly adopts the method of keyword filtering, however, such methods have little effect on the complex and ever-changing structure of darknet jargons. We propose a Chinese jargon detection framework based on unsupervised learning. The main idea is to compare similarity with high-dimensional word embedding features from different corpus to find jargons. Firstly, we collect data from six Chinese Tor websites to build a dark corpus dataset. Afterwards, we build a word-based pre-training model called DC-BERT, which can generate high-quality contextual word embeddings. Finally, we construct a cross-corpus jargon detection framework based on similarity analysis, which can effectively detect Chinese jargons in the darknet. The experimental results show that the proposed framework is both innovative and practical, reaching a detection accuracy of 91.5%.
Combining neural, statistical and external features for fake news stance identification
Authors: Gaurav Bhatt, Aman Sharma, Shivam Sharma, Ankush Nagpal, Balasubramanian Raman, Ankush Mittal
Venue: Conference WWW 2018
Year: 2018

Identifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news article as fake is still an open question as it is contingent on many factors which the current state-of-the-art models fail to incorporate. In this paper, we explore a subtask to fake news identification, and that is stance detection. Given a news article, the task is to determine the relevance of the body and its claim. We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem. We compute the neural embedding from the deep recurrent model, statistical features from the weighted n-gram bag-of-words model and handcrafted external features with the help of feature engineering heuristics. Finally, using deep neural layer all the features are combined, thereby classifying the headline-body news pair as agree, disagree, discuss, or unrelated. Through extensive experiments, we find that the proposed model outperforms all the state-of-the-art techniques including the submissions to the fake news challenge.
A temporal attentional model for rumor stance classification
Authors: Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, Daniel Lowd
Venue: Conference CIKM 2017
Year: 2017

Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.
A probabilistic exclusion principle for tracking multiple objects
Authors: John MacCormick, Andrew Blake 0001
Venue: Conference ICCV 1999
Year: 1999

Tracking multiple targets whose models are indistinguishable is a challenging problem. Simply instantiating several independent I-body trackers is not an adequate solution, because the independent trackers can coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.
Object localization by Bayesian correlation
Authors: Josephine Sullivan, Andrew Blake 0001, Michael Isard, John MacCormick
Venue: Conference ICCV 1999
Year: 1999

Maximisation of cross correlation is a commonly used principle for intensity based object localization that gives a single estimate of location. However, to facilitate sequential inference (e.g. over time or scale) and to allow the representation of ambiguity, it is desirable to represent an entire probability distribution for object location. Although the cross correlation itself (or some function of it) has sometimes been treated as a probability distribution, this is not generally justifiable. Bayesian correlation achieves a consistent probabilistic treatment by combining several developments. The first is the interpretation of correlation matching functions in probabilistic terms, as observation likelihoods. Second, probability distributions of filter bank responses are learned from training examples. Inescapably, response learning also demands statistical modelling of background intensities, and there are links here with image coding and Independent Component Analysis. Lastly, multi scale processing is achieved in a Bayesian context by means of a new algorithm, layered sampling, for which asymptotic properties are derived.
Probabilistic Tracking in a Metric Space
Authors: Kentaro Toyama, Andrew Blake 0001
Venue: Conference ICCV 2001
Year: 2001

A new exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especially temporal fusion, in a principled manner. Exemplars are selected representatives of raw training data, used here to represent probabilistic mixture distributions of object configurations. Their use avoids tedious hand-construction of object models and problems with changes of topology. Using exemplars in place of a parameterized model poses several challenges, addressed here with what we call the "Metric Mixture" (M/sup 2/) approach. The M/sup 2/ model has several valuable properties. Principally, it provides alternatives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space. Secondly, it uses a noise model that is learned from training data. Lastly, it eliminates any need for an assumption of probabilistic pixelwise independence. Experiments demonstrate the effectiveness of the M/sup 2/ model in two domains tracking walking people using chamfer distances on binary edge images and tracking mouth movements by means of a shuffle distance.
Diversity by proportionality: an election-based approach to search result diversification
Authors: Van Dang, W. Bruce Croft
Venue: Conference SIGIR 2012
Year: 2012

This paper presents a different perspective on diversity in search results: diversity by proportionality. We consider a result list most diverse, with respect to some set of topics related to the query, when the number of documents it provides on each topic is proportional to the topic's popularity. Consequently, we propose a framework for optimizing proportionality for search result diversification, which is motivated by the problem of assigning seats to members of competing political parties. Our technique iteratively determines, for each position in the result ranked list, the topic that best maintains the overall proportionality. It then selects the best document on this topic for this position. We demonstrate empirically that our method significantly outperforms the top performing approach in the literature not only on our proposed metric for proportionality, but also on several standard diversity measures. This result indicates that promoting proportionality naturally leads to minimal redundancy, which is a goal of the current diversity approaches.
Incorporating Explicit Subtopics in Personalized Search
Authors: Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, Ji-Rong Wen
Venue: Conference WWW 2023
Year: 2023

The key to personalized search is modeling user intents to tailor returned results for different users. Existing personalized methods mainly focus on learning implicit user interest vectors. In this paper, we propose ExpliPS, a personalized search model that explicitly incorporates query subtopics into personalization. It models the user’s current intent by estimating the user’s preference over the subtopics of the current query and personalizes the results over the weighted subtopics. We think that in such a way, personalized search could be more explainable and stable. Specifically, we first employ a semantic encoder to learn the representations of the user’s historical behaviours. Then with the historical behaviour representations, a subtopic preference encoder is devised to predict the user’s subtopic preferences on the current query. Finally, we rerank the candidates via a subtopic-aware ranker that prioritizes the documents relevant to the user-preferred subtopics. Experimental results show our model ExpliPS outperforms the state-of-the-art personalized web search models with explainable and stable results.
Visual textures as realizations of multivariate log-Gaussian Cox processes
Authors: Huu-Giao Nguyen, Ronan Fablet, Jean-Marc Boucher
Venue: Conference CVPR 2011
Year: 2011

In this paper, we address invariant keypoint-based texture characterization and recognition. Viewing keypoint sets associated with visual textures as realizations of point processes, we investigate probabilistic texture models from multivariate log-Gaussian Cox processes. These models are parameterized by the covariance structure of the spatial patterns. Their implementation initially rely on the construction of a codebook of the visual signatures of keypoints. We discuss invariance properties of the proposed models for texture recognition applications and report a quantitative evaluation for three texture datasets, namely: UIUC, KTH-TIPs and Brodatz. These experiments include a comparison of the performance reached using different methods for keypoint detection and characterization and demonstrate the relevance of the proposed models w.r.t. state-of-the-art methods. We further discuss the main contribution of proposed approach, including the key features of a statistical model and complexity aspects.
Autocalibration and uncalibrated reconstruction of shape from defocus
Authors: Yifei Lou, Paolo Favaro, Andrea L. Bertozzi, Stefano Soatto
Venue: Conference CVPR 2007
Year: 2007

Most algorithms for reconstructing shape from defocus assume that the images are obtained with a camera that has been previously calibrated so that the aperture, focal plane, and focal length are known. In this manuscript we characterize the set of scenes that can be reconstructed from defocused images regardless of calibration parameters. In lack of knowledge about the camera or about the scene, reconstruction is possible only up to an equivalence class that is described analytically. When weak knowledge about the scene is available, however, we show how it can be exploited in order to auto-calibrate the imaging device. This includes imaging a slanted plane or generic assumptions on the restoration of the deblurred images.
Simultaneous Searching of Globally Optimal Interacting Surfaces with Shape Priors
Authors: Qi Song 0001, Xiaodong Wu 0001, Yunlong Liu, Milan Sonka, Mona Kathryn Garvin
Venue: Conference CVPR 2010
Year: 2010

Multiple surface searching with only image intensity information is a difficult job in the presence of high noise and weak edges. We present in this paper a novel method for globally optimal multi-surface searching with a shape prior represented by convex pairwise energies. A 3-D graph-theoretic framework is employed. An arc-weighted graph is constructed based on a shape model built from training datasets. A wide spectrum of constraints is then incorporated. The shape prior term penalizes the local topological change from the original shape model. The globally optimal solution for multiple surfaces can be obtained by computing a maximum flow in low-order polynomial time. Compared with other graph-based methods, our approach provides more local and flexible control of the shape. We also prove that our algorithm can handle the detection of multiple crossing surfaces with no shared voxels. Our method was applied to several application problems, including medical image segmentation, scenic image segmentation, and image resizing. Compared with results without using shape prior information, our improvement was quite impressive, demonstrating the promise of our method.
Ovvv: Using virtual worlds to design and evaluate surveillance systems
Authors: Geoffrey R. Taylor, Andrew J. Chosak, Paul C. Brewer
Venue: Conference CVPR 2007
Year: 2007

Object video virtual video (OVVV) is a publicly available visual surveillance simulation test bed based on a commercial game engine. The tool simulates multiple synchronized video streams from a variety of camera configurations, including static, PTZ and omni-directional cameras, in a virtual environment populated with computer or player controlled humans and vehicles. To support performance evaluation, OVVV generates detailed automatic ground truth for each frame including target centroids, bounding boxes and pixel-wise foreground segmentation. We describe several realistic, controllable noise effects including pixel noise, video ghosting and radial distortion to improve the realism of synthetic video and provide additional dimensions for performance testing. Several indoor and outdoor virtual environments developed by the authors are described to illustrate the range of testing scenarios possible using OVVV. Finally, we provide a practical demonstration of using OVVV to develop and evaluate surveillance algorithms.
Discriminative virtual views for cross-view action recognition
Authors: Ruonan Li, Todd E. Zickler
Venue: Conference CVPR 2012
Year: 2012

We propose an approach for cross-view action recognition by way of `virtual views' that connect the action descriptors extracted from one (source) view to those extracted from another (target) view. Each virtual view is associated with a linear transformation of the action descriptor, and the sequence of transformations arising from the sequence of virtual views aims at bridging the source and target views while preserving discrimination among action categories. Our approach is capable of operating without access to labeled action samples in the target view and without access to corresponding action instances in the two views, and it also naturally incorporate and exploit corresponding instances or partial labeling in the target view when they are available. The proposed approach achieves improved or competitive performance relative to existing methods when instance correspondences or target labels are available, and it goes beyond the capabilities of these methods by providing some level of discrimination even when neither correspondences nor target labels exist.