Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching

3 December 2022

Papers citing "Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching"

8 / 8 papers shown

Title
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution Yi Huang Buse Giledereli Abdullatif Köksal Arzucan Özgür Elif Özkirimli VLM 67 77 0 10 Sep 2021
Comprehensive Comparative Study of Multi-Label Classification Methods Jasmin Bogatinovski L. Todorovski Jannis Brugger D. Kocev 43 115 0 14 Feb 2021
Label Confusion Learning to Enhance Text Classification Models Biyang Guo Songqiao Han Xiao Han Hailiang Huang Ting Lu 77 69 0 09 Dec 2020
The Emerging Trends of Multi-Label Learning Weiwei Liu Haobo Wang Xiaobo Shen Ivor W. Tsang 62 255 0 23 Nov 2020
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels Songhua Wu Xiaobo Xia Tongliang Liu Bo Han Biwei Huang Nannan Wang Haifeng Liu Gang Niu NoLa 33 53 0 14 Jun 2020
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers Iryna Gurevych 675 11,979 0 27 Aug 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy M. Lewis Luke Zettlemoyer Veselin Stoyanov AIMat 415 24,160 0 26 Jul 2019
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation Daniel Cer Mona T. Diab Eneko Agirre I. Lopez-Gazpio Lucia Specia 214 1,870 0 31 Jul 2017