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Cornelia Caragea

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26 papers
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26

AAAI Conference 2026 Conference Paper

On the Calibration of Image Semi-Supervised Learning Models

  • Mehrab Mustafy Rahman
  • Jayanth Mohan
  • Tiberiu Sosea
  • Cornelia Caragea

Semi-supervised learning (SSL) has demonstrated high performance in image classification tasks by effectively utilizing both labeled and unlabeled data. However, existing SSL methods often suffer from poor calibration, with models yielding overconfident predictions that misrepresent actual prediction likelihoods. Recently, neural networks trained with mixup that linearly interpolates random examples from the training set have shown better calibration in supervised settings. However, calibration of neural models remains under-explored in SSL settings. Although effective in supervised model calibration, random mixup of pseudolabels in SSL presents challenges due to the overconfidence and unreliability of pseudolabels. In this work, we introduce CalibrateMix, a targeted mixup-based approach that aims to improve the calibration of SSL models while maintaining or even improving their classification accuracy. Our method leverages training dynamics of labeled and unlabeled samples to identify ''easy-to-learn'' and ''hard-to-learn'' samples, which in turn are utilized in a targeted mixup of easy and hard samples. Experimental results across several benchmark datasets show that our method achieves lower expected calibration error (ECE) and superior accuracy compared to existing SSL approaches.

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ECAI Conference 2024 Conference Paper

FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding

  • Huitong Pan
  • Qi Zhang
  • Cornelia Caragea
  • Eduard C. Dragut
  • Longin Jan Latecki

Flowcharts are graphical tools for representing complex concepts in concise visual representations. This paper introduces the FlowLearn dataset, a resource tailored to enhance the understanding of flowcharts. FlowLearn contains complex scientific flowcharts and simulated flowcharts. The scientific subset contains 3, 858 flowcharts sourced from scientific literature and the simulated subset contains 10, 000 flowcharts created using a customizable script. The dataset is enriched with annotations for visual components, OCR, Mermaid code representation, and VQA question-answer pairs. Despite the proven capabilities of Large Vision-Language Models (LVLMs) in various visual understanding tasks, their effectiveness in decoding flowcharts—a crucial element of scientific communication—has yet to be thoroughly investigated. The FlowLearn test set is crafted to assess the performance of LVLMs in flowchart comprehension. Our study thoroughly evaluates state-of-the-art LVLMs, identifying existing limitations and establishing a foundation for future enhancements in this relatively underexplored domain. For instance, in tasks involving simulated flowcharts, GPT-4V achieved the highest accuracy (58%) in counting the number of nodes, while Claude recorded the highest accuracy (83%) in OCR tasks. Notably, no single model excels in all tasks within the FlowLearn framework, highlighting significant opportunities for further development.

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ICML Conference 2023 Conference Paper

Beam Tree Recursive Cells

  • Jishnu Ray Chowdhury
  • Cornelia Caragea

We propose Beam Tree Recursive Cell (BT-Cell) - a backpropagation-friendly framework to extend Recursive Neural Networks (RvNNs) with beam search for latent structure induction. We further extend this framework by proposing a relaxation of the hard top-$k$ operators in beam search for better propagation of gradient signals. We evaluate our proposed models in different out-of-distribution splits in both synthetic and realistic data. Our experiments show that BT-Cell achieves near-perfect performance on several challenging structure-sensitive synthetic tasks like ListOps and logical inference while maintaining comparable performance in realistic data against other RvNN-based models. Additionally, we identify a previously unknown failure case for neural models in generalization to unseen number of arguments in ListOps. The code is available at: https: //github. com/JRC1995/BeamTreeRecursiveCells.

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NeurIPS Conference 2023 Conference Paper

Efficient Beam Tree Recursion

  • Jishnu Ray Chowdhury
  • Cornelia Caragea

Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as an extension of Gumbel Tree RvNN and it was shown to achieve state-of-the-art length generalization performance in ListOps while maintaining comparable performance on other tasks. However, although better than previous approaches in terms of memory usage, BT-RvNN can be still exorbitantly expensive. In this paper, we identify the main bottleneck in BT-RvNN's memory usage to be the entanglement of the scorer function and the recursive cell function. We propose strategies to remove this bottleneck and further simplify its memory usage. Overall, our strategies not only reduce the memory usage of BT-RvNN by $10-16$ times but also create a new state-of-the-art in ListOps while maintaining similar performance in other tasks. In addition, we also propose a strategy to utilize the induced latent-tree node representations produced by BT-RvNN to turn BT-RvNN from a sentence encoder of the form $f: \mathbb{R}^{n \times d} \rightarrow \mathbb{R}^{d}$ into a token contextualizer of the form $f: \mathbb{R}^{n \times d} \rightarrow \mathbb{R}^{n \times d}$. Thus, our proposals not only open up a path for further scalability of RvNNs but also standardize a way to use BT-RvNNs as another building block in the deep learning toolkit that can be easily stacked or interfaced with other popular models such as Transformers and Structured State Space models. Our code is available at the link: https: //github. com/JRC1995/BeamRecursionFamily.

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AAAI Conference 2023 Conference Paper

Feature Normalization and Cartography-Based Demonstrations for Prompt-Based Fine-Tuning on Emotion-Related Tasks

  • Mahshid Hosseini
  • Cornelia Caragea

To train a model in a traditional supervised learning classification system for natural language processing (NLP) tasks, it is essential to have labeled data, which is not present in large amounts for many tasks. Prompt-based learning methods attempt to combat the supervised learning need for labeled data by directly adapting pre-trained language models and modeling the probability of text itself. In this paper, we propose a novel data-agnostic strategy for prompt-based fine-tuning that leverages feature moments (a.k.a., mean and standard deviation) as a data augmentation technique and employs training dynamics (i.e., confidence and variability) to allow more informative samples to be concatenated for generating demonstrations as input context. Our approach is a strong method for few-shot learning that forces the language model to pay special attention to the feature moments and allows more informative samples to be concatenated for generating demonstrations as input context by selecting high confidence and low variance samples. To demonstrate its effectiveness given limited training data, we conduct extensive experiments in different few-shot settings on three empathy and emotion classification datasets (from various domains). We further evaluate our method's robustness by introducing noise to our few-shot input data and labels and show that exchanging moments between samples and incorporating cartography-based demonstrations are beneficial when the available data is limited and noisy.

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AAAI Conference 2023 Short Paper

Label Smoothing for Emotion Detection (Student Abstract)

  • George Maratos
  • Tiberiu Sosea
  • Cornelia Caragea

Automatically detecting emotions from text has countless applications, ranging from large scale opinion mining to social robots in healthcare and education. However, emotions are subjective in nature and are often expressed in ambiguous ways. At the same time, detecting emotions can also require implicit reasoning, which may not be available as surface- level, lexical information. In this work, we conjecture that the overconfidence of pre-trained language models such as BERT is a critical problem in emotion detection and show that alleviating this problem can considerably improve the generalization performance. We carry out comprehensive experiments on four emotion detection benchmark datasets and show that calibrating our model predictions leads to an average improvement of 1.35% in weighted F1 score.

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ICML Conference 2023 Conference Paper

Monotonic Location Attention for Length Generalization

  • Jishnu Ray Chowdhury
  • Cornelia Caragea

We explore different ways to utilize position-based cross-attention in seq2seq networks to enable length generalization in algorithmic tasks. We show that a simple approach of interpolating the original and reversed encoded representations combined with relative attention allows near-perfect length generalization for both forward and reverse lookup tasks or copy tasks that had been generally hard to tackle. We also devise harder diagnostic tasks where the relative distance of the ideal attention position varies with timestep. In such settings, the simple interpolation trick with relative attention is not sufficient. We introduce novel variants of location attention building on top of Dubois et al. (2020) to address the new diagnostic tasks. We also show the benefits of our approaches for length generalization in SCAN (Lake & Baroni, 2018) and CFQ (Keysers et al. ,2020). Our code is available on GitHub.

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NeurIPS Conference 2023 Conference Paper

Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability

  • Jishnu Ray Chowdhury
  • Cornelia Caragea

Binary Balanced Tree Recursive Neural Networks (BBT-RvNNs) enforce sequence composition according to a preset balanced binary tree structure. Thus, their non-linear recursion depth (which is the tree depth) is just $\log_2 n$ ($n$ being the sequence length). Such logarithmic scaling makes BBT-RvNNs efficient and scalable on long sequence tasks such as Long Range Arena (LRA). However, such computational efficiency comes at a cost because BBT-RvNNs cannot solve simple arithmetic tasks like ListOps. On the flip side, RvNN models (e. g. , Beam Tree RvNN) that do succeed on ListOps (and other structure-sensitive tasks like formal logical inference) are generally several times more expensive (in time and space) than even Recurrent Neural Networks. In this paper, we introduce a novel framework --- Recursion in Recursion (RIR) to strike a balance between the two sides - getting some of the benefits from both worlds. In RIR, we use a form of two-level nested recursion - where the outer recursion is a $k$-ary balanced tree model with another recursive model (inner recursion) implementing its cell function. For the inner recursion, we choose Beam Tree RvNNs. To adjust Beam Tree RvNNs within RIR we also propose a novel strategy of beam alignment. Overall, this entails that the total recursive depth in RIR is upper-bounded by $k \log_k n$. Our best RIR-based model is the first model that demonstrates high ($\geq 90\%$) length-generalization performance on ListOps while at the same time being scalable enough to be trainable on long sequence inputs from LRA (it can reduce the memory usage of the original Beam Tree RvNN by hundreds of times). Moreover, in terms of accuracy in the LRA language tasks, it performs competitively with Structured State Space Models (SSMs) without any special initialization - outperforming Transformers by a large margin. On the other hand, while SSMs can marginally outperform RIR on LRA, they (SSMs) fail to length-generalize on ListOps. Our code is available at: https: //github. com/JRC1995/BeamRecursionFamily/

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AAAI Conference 2021 Conference Paper

It Takes Two to Empathize: One to Seek and One to Provide

  • Mahshid Hosseini
  • Cornelia Caragea

Empathy describes the capacity to feel, understand, and emotionally engage with what other people are experiencing. People have recently started to turn to online health communities to seek empathetic support when they undergo difficult situations such as suffering from a life-threatening disease, while others are there to provide empathetic support to those who need it. It is, therefore, important to detect the direction of empathy expressed in natural language. Previous studies only focus on the presence of empathy at a high-level and do not distinguish the direction of empathy that is expressed in textual messages. In this paper, we take one step further in the identification of perceived empathy from text by introducing IEMPATHIZE, a dataset of messages annotated with the direction of empathy exchanged in an online cancer network. We analyze user messages to identify the direction of empathy at a fine-grained level: seeking or providing empathy. Our dataset IEMPATHIZE serves as a challenging benchmark for studying empathy at a fine-grained level.

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ICML Conference 2021 Conference Paper

Modeling Hierarchical Structures with Continuous Recursive Neural Networks

  • Jishnu Ray Chowdhury
  • Cornelia Caragea

Recursive Neural Networks (RvNNs), which compose sequences according to their underlying hierarchical syntactic structure, have performed well in several natural language processing tasks compared to similar models without structural biases. However, traditional RvNNs are incapable of inducing the latent structure in a plain text sequence on their own. Several extensions have been proposed to overcome this limitation. Nevertheless, these extensions tend to rely on surrogate gradients or reinforcement learning at the cost of higher bias or variance. In this work, we propose Continuous Recursive Neural Network (CRvNN) as a backpropagation-friendly alternative to address the aforementioned limitations. This is done by incorporating a continuous relaxation to the induced structure. We demonstrate that CRvNN achieves strong performance in challenging synthetic tasks such as logical inference (Bowman et al. , 2015b) and ListOps (Nangia & Bowman, 2018). We also show that CRvNN performs comparably or better than prior latent structure models on real-world tasks such as sentiment analysis and natural language inference.

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AAAI Conference 2020 Short Paper

A Multi-Task Learning Approach to Sarcasm Detection (Student Abstract)

  • Edoardo Savini
  • Cornelia Caragea

Sarcasm detection plays an important role in natural language processing as it has been considered one of the most challenging subtasks in sentiment analysis and opinion mining applications. Our work aims to detect sarcasm in social media sites and discussion forums, exploiting the potential of deep neural networks and multi-task learning. Specifically, relying on the strong correlation between sarcasm and (implied negative) sentiment, we explore a multi-task learning framework that uses sentiment classification as an auxiliary task to inform the main task of sarcasm detection. Our proposed model outperforms many previous baseline methods on an existing large dataset annotated with sarcasm.

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AAAI Conference 2020 Short Paper

Exploring Abstract Concepts for Image Privacy Prediction in Social Networks (Student Abstract)

  • Gabriele Galfré
  • Cornelia Caragea

Automatically detecting the private nature of images posted in social networks such as Facebook, Flickr, and Instagram, is a long-standing goal considering the pervasiveness of these networks. Several prior works to image privacy prediction showed that object tags from images are highly informative about images’ privacy. However, we conjecture that other aspects of images captured by abstract concepts (e. g. , religion, sikhism, spirituality) can improve the performance of models that use only the concrete objects from an image (e. g. , temple and person). Experimental results on a Flickr dataset show that the abstract concepts and concrete object tags complement each other and yield the best performance when used in combination as features for image privacy prediction.

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AAAI Conference 2020 Conference Paper

On Identifying Hashtags in Disaster Twitter Data

  • Jishnu Ray Chowdhury
  • Cornelia Caragea
  • Doina Caragea

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short- Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as 92. 22%. The dataset, code, and other resources are available on Github. 1

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AAAI Conference 2019 Short Paper

Dynamically Identifying Deep Multimodal Features for Image Privacy Prediction

  • Ashwini Tonge
  • Cornelia Caragea

With millions of images shared online, privacy concerns are on the rise. In this paper, we propose an approach to image privacy prediction by dynamically identifying powerful features corresponding to objects, scene context, and image tags derived from Convolutional Neural Networks for each test image. Specifically, our approach identifies the set of most “competent” features on the fly, according to each test image whose privacy has to be predicted. Experimental results on thousands of Flickr images show that our approach predicts the sensitive (or private) content more accurately than the models trained on each individual feature set (object, scene, and tags alone) or their combination.

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TIST Journal 2019 Journal Article

Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction

  • Ashwini Tonge
  • Cornelia Caragea

Online images’ tags are very important for indexing, sharing, and searching of images, as well as surfacing images with private or sensitive content, which needs to be protected. Social media sites such as Flickr generate these metadata from user-contributed tags. However, as the tags are at the sole discretion of users, these tags tend to be noisy and incomplete. In this article, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images’ original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image, which are obtained from a large collection. Experimental results show that, although the user-input tags compose noise, our privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction and outperforms an existing privacy-oblivious approach to image tagging. The results also show that, even for images that do not have any user tags, our proposed approach can recommend accurate tags. Crowd-sourcing the predicted tags exhibits the quality of our privacy-aware recommended tags. Our code, features, and the dataset used in experiments are available at: https://github.com/ashwinitonge/privacy-aware-tag-rec.git.

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AAAI Conference 2018 Short Paper

Identifying Emotional Support in Online Health Communities

  • Hamed Khanpour
  • Cornelia Caragea
  • Prakhar Biyani

Extracting emotional support in Online Health Communities provides insightful information about patients’ emotional states. Current computational approaches to identifying emotional messages, i. e. , messages that contain emotional support, are typically based on a set of handcrafted features. In this paper, we show that high-level and abstract features derived from a combination of convolutional neural networks (CNN) with Long Short Term Memory (LSTM) networks can be successfully employed for emotional message identification and can obviate the need for handcrafted features.

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AAAI Conference 2018 Short Paper

Uncovering Scene Context for Predicting Privacy of Online Shared Images

  • Ashwini Tonge
  • Cornelia Caragea
  • Anna Squicciarini

With the exponential increase in the number of images that are shared online every day, the development of effective and efficient learning methods for image privacy prediction has become crucial. Prior works have used as features automatically derived object tags from images’ content and manually annotated user tags. However, we believe that in addition to objects, the scene context obtained from images’ content can improve the performance of privacy prediction. Hence, we propose to uncover scene-based tags from images’ content using convolutional neural networks. Experimental results on a Flickr dataset show that the scene tags and object tags complement each other and yield the best performance when used in combination with user tags.

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IJCAI Conference 2017 Conference Paper

A Group-Based Personalized Model for Image Privacy Classification and Labeling

  • Haoti Zhong
  • Anna Squicciarini
  • David Miller
  • Cornelia Caragea

We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate ``early warnings'' with respect to a user's privacy awareness level.

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AAAI Conference 2017 Short Paper

A Position-Biased PageRank Algorithm for Keyphrase Extraction

  • Corina Florescu
  • Cornelia Caragea

Given the large amounts of online textual documents available these days, e. g. , news articles and scientific papers, effective methods for extracting keyphrases, which provide a highlevel topic description of a document, are greatly needed. We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word’s occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.

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TIST Journal 2017 Journal Article

Vertical Ensemble Co-Training for Text Classification

  • Gilad Katz
  • Cornelia Caragea
  • Asaf Shabtai

High-quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling additional samples could be expensive and time consuming. Co-training algorithms, which make use of unlabeled data to improve classification, have proven to be very effective in such cases. Generally, co-training algorithms work by using two classifiers, trained on two different views of the data, to label large amounts of unlabeled data. Doing so can help minimize the human effort required for labeling new data, as well as improve classification performance. In this article, we propose an ensemble-based co-training approach that uses an ensemble of classifiers from different training iterations to improve labeling accuracy. This approach, which we call vertical ensemble, incurs almost no additional computational cost. Experiments conducted on six textual datasets show a significant improvement of over 45% in AUC compared with the original co-training algorithm.

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IJCAI Conference 2016 Conference Paper

Content-Driven Detection of Cyberbullying on the Instagram Social Network

  • Haoti Zhong
  • Hao Li
  • Anna Squicciarini
  • Sarah Rajtmajer
  • Christopher Griffin
  • David Miller
  • Cornelia Caragea

We study detection of cyberbullying in photo-sharing networks, with an eye on developing early warning mechanisms for the prediction of posted images vulnerable to attacks. Given the overwhelming increase in media accompanying text in online social networks, we investigate use of posted images and captions for improved detection of bullying in response to shared content. We validate our approaches on a dataset of over 3000 images along with peer-generated comments posted on the Instagram photo-sharing network, running comprehensive experiments using a variety of classifiers and feature sets. In addition to standard image and text features, we leverage several novel features including topics determined from image captions and a pretrained convolutional neural network on image pixels. We identify the importance of these advanced features in assisting detection of cyberbullying in posted comments. We also provide results on classification of images and captions themselves as potential targets for cyberbullies.

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AAAI Conference 2016 Conference Paper

Image Privacy Prediction Using Deep Features

  • Ashwini Tonge
  • Cornelia Caragea

Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online, the development of effective and efficient prediction methods for image privacy settings are highly needed. In this study, we explore deep visual features and deep image tags for image privacy prediction. The results of our experiments show that models trained on deep visual features outperform those trained on SIFT and GIST. The results also show that deep image tags combined with user tags perform best among all tested features.

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AAAI Conference 2014 Conference Paper

Extracting Keyphrases from Research Papers Using Citation Networks

  • Sujatha Das Gollapalli
  • Cornelia Caragea

Keyphrases for a document concisely describe the document using a small set of phrases. Keyphrases were previously shown to improve several document processing and retrieval tasks. In this work, we study keyphrase extraction from research papers by leveraging citation networks. We propose CiteTextRank for keyphrase extraction from research articles, a graph-based algorithm that incorporates evidence from both a document’s content as well as the contexts in which the document is referenced within a citation network. Our model obtains significant improvements over the state-of-the-art models for this task. Specifically, on several datasets of research papers, CiteTextRank improves precision at rank 1 by as much as 9-20% over state-of-the-art baselines.

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AAAI Conference 2012 Conference Paper

Combining Hashing and Abstraction in Sparse High Dimensional Feature Spaces

  • Cornelia Caragea
  • Adrian Silvescu
  • Prasenjit Mitra

With the exponential increase in the number of documents available online, e. g. , news articles, weblogs, scientific documents, the development of effective and efficient classification methods is needed. The performance of document classifiers critically depends, among other things, on the choice of the feature representation. The commonly used “bag of words” and n-gram representations can result in prohibitively high dimensional input spaces. Data mining algorithms applied to these input spaces may be intractable due to the large number of dimensions. Thus, dimensionality reduction algorithms that can process data into features fast at runtime, ideally in constant time per feature, are greatly needed in high throughput applications, where the number of features and data points can be in the order of millions. One promising line of research to dimensionality reduction is feature clustering. We propose to combine two types of feature clustering, namely hashing and abstraction based on hierarchical agglomerative clustering, in order to take advantage of the strengths of both techniques. Experimental results on two text data sets show that the combined approach uses significantly smaller number of features and gives similar performance when compared with the “bag of words” and n-gram approaches.

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IJCAI Conference 2011 Conference Paper

Context Sensitive Topic Models for Author Influence in Document Networks

  • Saurabh Kataria
  • Prasenjit Mitra
  • Cornelia Caragea
  • C. Lee Giles

Since the seminal work of Sampath et al. in 1996, despite the subsequent flourishing of techniques on diagnosis of discrete-event systems (DESs), the basic notions of fault and diagnosis have been remaining conceptually unchanged. Faults are defined at component level and diagnoses incorporate the occurrences of component faults within system evolutions: diagnosis is context-free. As this approach may be unsatisfactory for a complex DES, whose topology is organized in a hierarchy of abstractions, we propose to define different diagnosis rules for different subsystems in the hierarchy. Relevant fault patterns are specified as regular expressions on patterns of lower-level subsystems. Separation of concerns is achieved and the expressive power of diagnosis is enhanced: each subsystem has its proper set of diagnosis rules, which may or may not depend on the rules of other subsystems. Diagnosis is no longer anchored to components: it becomes context-sensitive. The approach yields seemingly contradictory but nonetheless possible scenarios: a subsystem can be normal despite the faulty behavior of a number of its components (positive paradox); also, it can be faulty despite the normal behavior of all its components (negative paradox).

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AAAI Conference 2005 Short Paper

Learning Support Vector Machines from Distributed Data Sources

  • Cornelia Caragea

In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.

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