In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

Entities are at the center of how we represent and aggregate knowledge. For instance,
Encyclopedias such as Wikipedia are structured by entities (eg, one per Wikipedia article) …

We introduce CM3, a family of causally masked generative models trained over a large
corpus of structured multi-modal documents that can contain both text and image tokens …

Massively multilingual transformers pretrained with language modeling objectives (eg,
mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross …

Abstract Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word
in context by identifying the most suitable meaning from a predefined sense inventory …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a
particular context. Traditional supervised methods rarely take into consideration the lexical …

Entity Linking (EL) is an essential task for semantic text understanding and information
extraction. Popular methods separately address the Mention Detection (MD) and Entity …

An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …