Taxonomy completion, a self-supervised task, aims to add new concepts to an existing
taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers …

Automatic taxonomy completion aims to attach the emerging concept to an appropriate pair
of hypernym and hyponym in the existing taxonomy. Existing methods suffer from the …

Taxonomy completion, enriching existing taxonomies by inserting new concepts as parents
or attaching them as children, has gained significant interest. Previous approaches embed …

Massive amount of unstructured text data are generated daily, ranging from news articles to
scientific papers. How to mine structured knowledge from the text data remains a crucial …

Fine-grained entity ty** (FET), which assigns entities in text with context-sensitive, fine-
grained semantic types, is a basic but important task for knowledge extraction from …

Taxonomy expansion is a primary method for enriching taxonomies, involving appending a
large number of additional nodes (ie, queries) to an existing taxonomy (ie, seed), with the …

Document retrieval has greatly benefited from the advancements of large-scale pre-trained
language models (PLMs). However, their effectiveness is often limited in theme-specific …

The sparse interactions between users and items have aggravated the difficulty of their
representations in recommender systems. Existing methods leverage tags to alleviate the …

Modeling trajectory data with generic-purpose dense representations has become a
prevalent paradigm for various downstream applications, such as trajectory classification …

Taxonomy completion aims to integrate new concepts into existing taxonomies by
determining their appropriate hypernym and hyponym. While semantic and structural …