A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Financial time series forecasting with multi-modality graph neural network
Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …
investment decisions. This is a challenging task as the problems are always accompanied …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
A survey on deep learning event extraction: Approaches and applications
Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …
from massive textual data. With the rapid development of deep learning, EE based on deep …
What is event knowledge graph: A survey
Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …
Knowledge graph-based event embedding framework for financial quantitative investments
Event representative learning aims to embed news events into continuous space vectors for
capturing syntactic and semantic information from text corpus, which is benefit to event …
capturing syntactic and semantic information from text corpus, which is benefit to event …
Claret: Pre-training a correlation-aware context-to-event transformer for event-centric generation and classification
Generating new events given context with correlated ones plays a crucial role in many event-
centric reasoning tasks. Existing works either limit their scope to specific scenarios or …
centric reasoning tasks. Existing works either limit their scope to specific scenarios or …
A graph propagation model with rich event structures for joint event relation extraction
The task of event relation extraction (ERE) aims to organize multiple events and their
relations as a directed graph. However, existing ERE methods exhibit two limitations:(1) …
relations as a directed graph. However, existing ERE methods exhibit two limitations:(1) …
Discos: Bridging the gap between discourse knowledge and commonsense knowledge
Commonsense knowledge is crucial for artificial intelligence systems to understand natural
language. Previous commonsense knowledge acquisition approaches typically rely on …
language. Previous commonsense knowledge acquisition approaches typically rely on …
Improving event representation via simultaneous weakly supervised contrastive learning and clustering
Representations of events described in text are important for various tasks. In this work, we
present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering …
present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering …