Search Results for author:
Found 11 papers, 6 papers with code
SCStory: Self-supervised and Continual Online Story Discovery
With a lightweight hierarchical embedding module that first learns sentence representations and then article representations, SCStory identifies story-relevant information of news articles and uses them to discover stories.
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
We identify the inadequacy of universal and specific prompting in handling these dynamic shifts.
Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding
Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations.
MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities
By preserving the heterogeneity of potential classes, MEGClass can select the most informative class-indicative documents as iterative feedback to enhance the initial word-based class representations and ultimately fine-tune a pre-trained text classifier.
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set.
MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction
Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk to reduce future accidents.
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications.
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures.
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Time-series data are ubiquitous these days, but lack of the labels in time-series data is regarded as a hurdle for its broad applicability.
MLAT: Metric Learning for kNN in Streaming Time Series
Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks.
