Recent progress in leveraging deep learning methods for question answering

T Hao, X Li, Y He, FL Wang, Y Qu - Neural Computing and Applications, 2022 - Springer
Question answering, serving as one of important tasks in natural language processing,
enables machines to understand questions in natural language and answer the questions …

Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF

Y An, X ** with weak supervision from a masked language model
H Dai, Y Song, H Wang - ar** by using a richer and ultra-fine
set of types, and labeling noun phrases including pronouns and nominal nouns instead of …

Plug-and-play knowledge injection for pre-trained language models

Z Zhang, Z Zeng, Y Lin, H Wang, D Ye, C ** with a type taxonomy: a systematic review
R Wang, F Hou, SF Cahan, L Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Fine-grained entity ty** (FGET) is an important natural language processing (NLP) task. It
is to assign fine-grained semantic types of a type taxonomy (eg, Person/artist/actor) to entity …

A neighborhood-attention fine-grained entity ty** for knowledge graph completion

J Zhuo, Q Zhu, Y Yue, Y Zhao, W Han - … on web search and data mining, 2022 - dl.acm.org
Knowledge graph (KG) entity ty** focuses on inferring possible entity type instances,
which is a significant subtask of knowledge graph completion (KGC). Existing entity ty** …

CORE: A knowledge graph entity type prediction method via complex space regression and embedding

X Ge, YC Wang, B Wang, CCJ Kuo - Pattern Recognition Letters, 2022 - Elsevier
Entity type prediction is an important problem in knowledge graph (KG) research. A new KG
entity type prediction method, named CORE (COmplex space Regression and E mbedding) …