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Found 28 papers, 13 papers with code
Citations and Trust in LLM Generated Responses
Question answering systems are rapidly advancing, but their opaque nature may impact user trust.
Fear and Loathing on the Frontline: Decoding the Language of Othering by Russia-Ukraine War Bloggers
Othering, the act of portraying outgroups as fundamentally different from the ingroup, often escalates into framing them as existential threats--fueling intergroup conflict and justifying exclusion and violence.
MedCodER: A Generative AI Assistant for Medical Coding
Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors.
Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War
Existing research, however, often fails to capture information narrative evolution, overlooking both the fluid nature of narratives and the internal mechanisms that drive their evolution.
Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery
Electronic Discovery (eDiscovery) involves identifying relevant documents from a vast collection based on legal production requests.
Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction
Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels.
ChatEL: Entity Linking with Chatbots
Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well.
EntGPT: Linking Generative Large Language Models with Knowledge Bases
Additionally, our instruction tuning method (EntGPT-I) improves micro-F_1 scores by 2. 1% on average in supervised EL tasks and outperforms several baseline models in six Question Answering tasks.
Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data
Recent decisions to discontinue access to social media APIs are having detrimental effects on Internet research and the field of computational social science as a whole.
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
In the present work, we describe Top-K K-Nearest Neighbor (TK-KNN), which uses a more robust pseudo-labeling approach based on distance in the embedding space while maintaining a balanced set of pseudo-labeled examples across classes through a ranking-based approach.
Dynamic Vertex Replacement Grammars
Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data.
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
MEWS: Real-time Social Media Manipulation Detection and Analysis
This article presents a beta-version of MEWS (Misinformation Early Warning System).
Motif Mining: Finding and Summarizing Remixed Image Content
On the internet, images are no longer static; they have become dynamic content.
Posthoc Verification and the Fallibility of the Ground Truth
Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches.
Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels
We refer to such tasks as survey settings because the ground truth is defined through a survey of one or more human raters.
Reddit Entity Linking Dataset
We introduce and make publicly available an entity linking dataset from Reddit that contains 17, 316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement.
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER
The training process of scientific NER models is commonly performed in two steps: i) Pre-training a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data.
HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure.
Automatic Discovery of Political Meme Genres with Diverse Appearances
This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres.
Improved Forecasting of Cryptocurrency Price using Social Signals
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics.
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility.
Visualizing the Flow of Discourse with a Concept Ontology
Understanding and visualizing human discourse has long being a challenging task.
Open-World Knowledge Graph Completion
Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking.
ProjE: Embedding Projection for Knowledge Graph Completion
In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function.
Growing Graphs with Hyperedge Replacement Graph Grammars
In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs.
Scalable Models for Computing Hierarchies in Information Networks
Information hierarchies are organizational structures that often used to organize and present large and complex information as well as provide a mechanism for effective human navigation.
Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs
Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information.
