Log-linear models and more recently neural network models used for supervised relation
extraction requires substantial amounts of training data and time, limiting the portability to …

Active learning methods which present selected examples from the corpus for annotation
provide more efficient learning of supervised relation extraction models, but they leave the …

Recent graph-based models for joint multiple intent detection and slot filling have obtained
promising results through modeling the guidance from the prediction of intents to the …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

Large unbalanced datasets pose challenges for machine learning models, as redundant
and irrelevant features can hinder their effectiveness. Furthermore, the performance of …

Relation extraction is an important task in information extraction and retrieval that aims to
extract relations among the given entities from running texts. To achieve a good …

Table filling based joint named entity recognition and relation extraction task aims to share
representation of subtasks in a table to extract structured knowledge. However, most of …

Document-level relation extraction aims to infer complex semantic relations among entities
in an entire document. Compared with the sentence-level relation extraction, document-level …

Relation extraction is a crucial step in the constructions of knowledge graphs (KGs).
However, relation extraction is performed manually in the manufacturing field due to the …

Radiology reports play a critical role in communicating medical findings to physicians. In
each report, the impression section summarizes essential radiology findings. In clinical …