Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Information extraction suffers from its varying targets, heterogeneous structures, and
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …

The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or
phrases that describe the appropriate types of entities mentioned in sentences. A key …

Few-shot NER needs to effectively capture information from limited instances and transfer
useful knowledge from external resources. In this paper, we propose a self-describing …

Semantic typing aims at classifying tokens or spans of interest in a textual context into
semantic categories such as relations, entity types, and event types. The inferred labels of …

Conceptualization, or viewing entities and situations as instances of abstract concepts in
mind and making inferences based on that, is a vital component in human intelligence for …

We investigate the knowledge graph entity typing task which aims at inferring plausible
entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) …

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained
level for entity mentions based on their contextual information. Conventional methods for …

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions
according to their context, which is a fundamental task in various entity-leveraging …

Entity typing aims at predicting one or more words that describe the type (s) of a specific
mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and …