Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

As an effective approach to tune pre-trained language models (PLMs) for specific tasks,
prompt-learning has recently attracted much attention from researchers. By using\textit …

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to
predict a set of free-form phrases (eg skyscraper, songwriter, or criminal) that describe …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

Neural entity typing models typically represent fine-grained entity types as vectors in a high-
dimensional space, but such spaces are not well-suited to modeling these types' complex …

Many tasks in natural language processing can be viewed as multi-label classification
problems. However, most of the existing models are trained with the standard cross-entropy …

The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a
hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus …

Extracting structured data from HTML documents is a long-studied problem with a broad
range of applications like augmenting knowledge bases, supporting faceted search, and …

Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a
broad set of types. Distant supervision paradigm is extensively used to generate training …

Distantly-labeled data can be used to scale up training of statistical models, but it is typically
noisy and that noise can vary with the distant labeling technique. In this work, we propose a …