Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis
W **, B Zhao, L Zhang, C Liu, H Yu - Information Processing & …, 2023 - Elsevier
Abstract Aspect-based Sentiment Analysis (ABSA) is a crucial natural language
understanding (NLU) research field which aims to accurately recognize reviewers' opinions …
understanding (NLU) research field which aims to accurately recognize reviewers' opinions …
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 …
Maven-ere: A unified large-scale dataset for event coreference, temporal, causal, and subevent relation extraction
The diverse relationships among real-world events, including coreference, temporal, causal,
and subevent relations, are fundamental to understanding natural languages. However, two …
and subevent relations, are fundamental to understanding natural languages. However, two …
Kept: Knowledge enhanced prompt tuning for event causality identification
Event causality identification (ECI) aims to identify causal relations of event mention pairs in
text. Despite achieving certain accomplishments, existing methods are still not effective due …
text. Despite achieving certain accomplishments, existing methods are still not effective due …
ERGO: Event relational graph transformer for document-level event causality identification
Document-level Event Causality Identification (DECI) aims to identify causal relations
between event pairs in a document. It poses a great challenge of across-sentence reasoning …
between event pairs in a document. It poses a great challenge of across-sentence reasoning …
Semantic structure enhanced event causality identification
Event Causality Identification (ECI) aims to identify causal relations between events in
unstructured texts. This is a very challenging task, because causal relations are usually …
unstructured texts. This is a very challenging task, because causal relations are usually …
CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
Abstract Document-level Event Causality Identification (DECI) aims to recognize causal
relations between events within a document. Recent studies focus on building a document …
relations between events within a document. Recent studies focus on building a document …
Zero-shot cross-lingual document-level event causality identification with heterogeneous graph contrastive transfer learning
Event Causality Identification (ECI) refers to the detection of causal relations between events
in texts. However, most existing studies focus on sentence-level ECI with high-resource …
in texts. However, most existing studies focus on sentence-level ECI with high-resource …
Enhancing event causality identification with counterfactual reasoning
Existing methods for event causality identification (ECI) focus on mining potential causal
signals, ie, causal context keywords and event pairs. However, causal signals are …
signals, ie, causal context keywords and event pairs. However, causal signals are …