arXiv subjects

Wenkai Zhang

Publications and source records attributed to Wenkai Zhang.

21 records · Page 2Linked to original sources

arXiv2021

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.

cs.CL↗

arXiv2021

DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving

It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment. However, there are currently two main challenges about the above relationship prediction: i) The traditional heuristic methods based on IoU can hardly deal with the traffic jam scenarios for the occlusion. ii) It is difficult to establish an efficient applicable solution for the vehicle-mounted system. To address these issues, we propose an innovative relationship prediction method, namely DeepWORD, by designing a graph convolution network (GCN). Specifically, we utilize the feature maps with local correlation as the input of nodes to improve the information richness. Besides, we introduce the graph attention network (GAT) to dynamically amend the prior estimation deviation. Furthermore, we establish an annotated owner-member relationship dataset called WORD as a large-scale benchmark, which will be available soon. The experiments demonstrate that our solution achieves state-of-the-art accuracy and real-time in practice.

cs.CV↗

arXiv2015

Extended fast search clustering algorithm: widely density clusters, no density peaks

CFSFDP (clustering by fast search and find of density peaks) is recently developed density-based clustering algorithm. Compared to DBSCAN, it needs less parameters and is computationally cheap for its non-iteration. Alex. at al have demonstrated its power by many applications. However, CFSFDP performs not well when there are more than one density peak for one cluster, what we name as "no density peaks". In this paper, inspired by the idea of a hierarchical clustering algorithm CHAMELEON, we propose an extension of CFSFDP,E_CFSFDP, to adapt more applications. In particular, we take use of original CFSFDP to generating initial clusters first, then merge the sub clusters in the second phase. We have conducted the algorithm to several data sets, of which, there are "no density peaks". Experiment results show that our approach outperforms the original one due to it breaks through the strict claim of data sets.

cs.DS↗