Results 231 to 240 of about 353,852 (269)

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data. [PDF]

Clin Cancer Res
Howard FM, Fasching PA, Santa-Maria CA, Lim E, Sparano JA, Lustberg MB, Bachelot T, Blyuss O, Brezden-Masley C, Park YH, Akdere M, Ye F, Pantoja K, Kurz C, Dominguez Castro P, Razavi P.   +15 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Overview of Distant Supervised Relation Extraction

2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2021
Relation extraction is a fundamental task in natural language processing, aiming at extracting relational triples from plain text. However, there are fewer instances in the manually constructed dataset to meet the learning needs of relation extraction models.
Xiang Li, Junan Yang, Hui Liu, Zongwei Liang, Lingzhi Qu, Yu Zhang, Kun Shao, Dongxing Zhao   +7 more
openaire   +1 more source

Enhanced Distant Supervised Open Information Extraction

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 2021
In this paper, we proposed a new Open IE task which is extracting triplets from sentences with a given head entity. In order to make use of triplets in knowledge base for Open IE, we intensify the distant supervision assume that the two entities and the predict word should be in the same sentence when we construct the distant supervision dataset.
Hongbing Hu, Qizhou Xing, Ming Chen
openaire   +1 more source

Curriculum Learning for Distant Supervision Relation Extraction

SSRN Electronic Journal, 2020
Abstract Relation extraction under distant supervision leverages the existing knowledge base to label data automatically, thus greatly reduced the consumption of human labors. Although distant supervision is an efficient method to obtain a large amount of labeled data, the training dataset labeled by distant supervision suffers from noise problem ...
Qiongxin Liu, Peng Wang, Jiasheng Wang, Jing Ma   +3 more
openaire   +1 more source

Building Distant Supervised Relation Extractors

2014 IEEE International Conference on Semantic Computing, 2014
A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published.
Thiago Nunes, Daniel Schwabe
openaire   +1 more source

Global Distant Supervision for Relation Extraction

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
Machine learning approaches to relation extraction are typically supervised and require expensive labeled data. To break the bottleneck of labeled data, a promising approach is to exploit easily obtained indirect supervision knowledge – which we usually refer to as distant supervision (DS).
Xianpei Han, Le Sun
openaire   +1 more source