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Found 44 papers, 13 papers with code
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents
Therefore, we propose the first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training.
Are BERTs Sensitive to Native Interference in L2 Production?
With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners’ native languages.
WildGraph: Realistic Graph-based Trajectory Generation for Wildlife
Trajectory generation is an important task in movement studies; it circumvents the privacy, ethical, and technical challenges of collecting real trajectories from the target population.
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents
To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource.
Optical Character Recognition
Optical Character Recognition (OCR)
Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records
To reduce human intervention and improve the framework design, we propose an automated approach named AutoDP, which can search for the optimal configuration of task grouping and architectures simultaneously.
Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions
Sentiment Analysis (SA) refers to the task of associating a view polarity (usually, positive, negative, or neutral; or even fine-grained such as slightly angry, sad, etc.)
Leveraging External Knowledge Resources to Enable Domain-Specific Comprehension
Machine Reading Comprehension (MRC) has been a long-standing problem in NLP and, with the recent introduction of the BERT family of transformer based language models, it has come a long way to getting solved.
WildGEN: Long-horizon Trajectory Generation for Wildlife
Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies.
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering
Domain adaptation, the process of training a model in one domain and applying it to another, has been extensively explored in machine learning.
Analysis of Elephant Movement in Sub-Saharan Africa: Ecological, Climatic, and Conservation Perspectives
The interaction between elephants and their environment has profound implications for both ecology and conservation strategies.
Spatio-temporal Storytelling? Leveraging Generative Models for Semantic Trajectory Analysis
In this paper, we lay out a vision for analysing semantic trajectory traces and generating synthetic semantic trajectory data (SSTs) using generative language model.
Lumos in the Night Sky: AI-enabled Visual Tool for Exploring Night-Time Light Patterns
We introduce NightPulse, an interactive tool for Night-time light (NTL) data visualization and analytics, which enables researchers and stakeholders to explore and analyze NTL data with a user-friendly platform.
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users.
Exploring Descriptions of Movement Through Geovisual Analytics
We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques.
Recognition of Implicit Geographic Movement in Text
Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research.
Federated Unlearning with Knowledge Distillation
Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data.
Differentiating Geographic Movement Described in Text Documents
Understanding movement described in text documents is important since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more.
An Analysis of Elephants' Movement Data in Sub-Saharan Africa Using Clustering
We explore the use of clustering to identify locations of interest to African Elephants in regions of Sub-Saharan Africa.
Learning To Describe Player Form in The MLB
Major League Baseball (MLB) has a storied history of using statistics to better understand and discuss the game of baseball, with an entire discipline of statistics dedicated to the craft, known as sabermetrics.
Extractive Research Slide Generation Using Windowed Labeling Ranking
Presentation slides describing the content of scientific and technical papers are an efficient and effective way to present that work.
Mitigating Backdoor Attacks in Federated Learning
To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6. 4%, with only a 1. 7% loss of test accuracy.
Repurposing TREC-COVID Annotations to Answer the Key Questions of CORD-19
Seeing the related endeavors, we set out to repurpose the relevancy annotations for TREC-COVID tasks to identify journal articles in CORD-19 which are relevant to the key questions posed by CORD-19.
Extractive Summarizer for Scholarly Articles
We introduce an extractive method that will summarize long scientific papers.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
In this paper, we study a novel problem of explainable user engagement prediction for social network Apps.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values.
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.
Ranked #25 on
Node Classification
on Pubmed
Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset.
Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning
A significant proportion of individuals' daily activities is experienced through digital devices.
Summarizing Situational and Topical Information During Crises
The use of microblogging platforms such as Twitter during crises has become widespread.
Applications of Online Deep Learning for Crisis Response Using Social Media Information
During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes.
Generating Abstractive Summaries from Meeting Transcripts
The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment.
Multi-document abstractive summarization using ILP based multi-sentence compression
The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences.
Abstractive Meeting Summarization UsingDependency Graph Fusion
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries.
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages
Scarcity of labeled data causes poor performance in machine training.
A neural probabilistic model for context based citation recommendation
It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context.
