Results 151 to 160 of about 2,273 (194)

Corrigendum to ‘MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos’ [Exp. Syst. Appl. 262 (2025) 125695]

Atiya Usmani, Saeed Hamood Alsamhi, Muhammad Jaleed Khan, John G. Breslin, Edward Curry   +4 more
openalex   +1 more source

A novel framework for TER allocation using multilayer perceptron and intuitionistic fuzzy Z numbers for talent management. [PDF]

Sci Rep
Yang T.
europepmc   +1 more source

A novel multi-modal retrieval framework for tracking vehicles using natural language descriptions. [PDF]

PLoS One
Zhang C, Liu Z, Li K, Li Y, Qi X, Ding N.   +5 more
europepmc   +1 more source

Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction

Association for Computational Linguistics 2025, Du, Kounianhua, Fu, Lingyue, Hou, Faceia, Hu, Hai, Meng, Chenhao   +5 more
openalex   +1 more source

Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction [PDF]

Zongming Cai, Jianhang Tang, Zhenyong Zhang, Jinghui Qin, Kebing Jin, Hankz Hankui Zhuo   +5 more
openalex  

LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach. [PDF]

PLoS One
Minello G, Santagiustina CRMA, Warglien M.   +2 more
europepmc   +1 more source

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation. [PDF]

Front Robot AI
Lakhnati Y, Pascher M, Gerken J.
europepmc   +1 more source

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

IEEE Transactions on Affective Computing
Li Yuan, Jin Wang 0008, Liang-Chih Yu, Xuejie Zhang 0002   +3 more
openaire   +1 more source

The role of natural language processing in cancer care: a systematic scoping review with narrative synthesis

Sun M, Reiter E, Duncan L, Adam R.
europepmc   +1 more source

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach.

JMIR Ment Health
Salmi S, Mérelle S, Gilissen R, van der Mei R, Bhulai S.   +4 more
europepmc   +1 more source