Event extraction (EE) is a crucial information extraction task that aims to extract event
information in texts. Previous methods for EE typically model it as a classification task, which …

Long document coreference resolution remains a challenging task due to the large memory
and runtime requirements of current models. Recent work doing incremental coreference …

The task of text classification is usually divided into two stages: text feature extraction and
classification. In this standard formalization, categories are merely represented as indexes in …

Existing studies typically handle aspect-based sentiment analysis by stacking multiple
neural modules, which inevitably result in severe error propagation. Instead, we propose a …

Entity linking (EL) for the rapidly growing short text (eg search queries and news titles) is
critical to industrial applications. Most existing approaches relying on adequate context for …

Extracting entities and their relationships from financial documents is crucial for analyzing
and predicting future market trends. However, the current state of the art in this field faces …

Accurate and efficient extraction of key information related to diseases from medical
examination reports, such as X-ray and ultrasound images, CT scans, and others, is crucial …

This paper proposes a new dataset, MuDoCo, composed of authored dialogs between a
fictional user and a system who are given tasks to perform within six task domains. These …

Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their
anaphoric mentions, while non-zero coreference resolution targets at clustering mentions …

Zero-resource named entity recognition (NER) severely suffers from data scarcity in a
specific domain or language. Most studies on zero-resource NER transfer knowledge from …