Search Results for author:
Found 27 papers, 7 papers with code
Locally Adaptive Translation for Knowledge Graph Embedding
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.
Predict Anchor Links across Social Networks via an Embedding Approach
Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation.
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
Document-level information is very important for event detection even at sentence level.
Event Detection with Multi-Order Graph Convolution and Aggregated Attention
Syntactic relations are broadly used in many NLP tasks.
NeuInfer: Knowledge Inference on N-ary Facts
It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings
To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments.
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity).
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction.
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 knowledge representation form like Event KG (EKG).
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.
Rich Event Modeling for Script Event Prediction
Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants.
Few-shot Link Prediction on N-ary Facts
Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs).
Semantic Structure Enhanced Event Causality Identification
This is a very challenging task, because causal relations are usually expressed by implicit associations between events.
Nested Event Extraction upon Pivot Element Recogniton
Since existing NEE datasets (e. g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest.
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations.
An In-Context Schema Understanding Method for Knowledge Base Question Answering
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base.
Retrieval-Augmented Code Generation for Universal Information Extraction
Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks.
MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse Worlds
Given the low cost of our method, we are able to conduct a large scale study regarding LLM alignment issues in different worlds.
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction
After instruction tuning, KnowCoder further exhibits strong generalization ability on unseen schemas and achieves up to $\textbf{12. 5%}$ and $\textbf{21. 9%}$, compared to sota baselines, under the zero-shot setting and the low resource setting, respectively.
Class-Incremental Few-Shot Event Detection
Therefore, this paper proposes a new task, called class-incremental few-shot event detection.
Self-Improvement Programming for Temporal Knowledge Graph Question Answering
Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs).
Selective Temporal Knowledge Graph Reasoning
Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently.
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification
For the meta learner, it requires a good generalization ability so as to quickly adapt to new tasks.
