A Boundary Regressing Model for Nested Named Entity Recognition

by   Yanping Chen, et al.

Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for an NE candidate in a sentence. In shallow structures, categorized features are weighted to support the prediction. Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations. This approach unfolds a dense space saturated with high-order abstract semantic information, where the prediction is based on distributed feature representations. In this paper, the regression operation is introduced to locate NEs in a sentence. In this approach, a deep network is first designed to transform an input sentence into recurrent feature maps. Bounding boxes are generated from the feature maps, where a box is an abstract representation of an NE candidate. In addition to the class tag, each bounding box has two parameters denoting the start position and the length of an NE candidate. In the training process, the location offset between a bounding box and a true NE are learned to minimize the location loss. Based on this motivation, a multiobjective learning framework is designed to simultaneously locate entities and predict the class probability. By sharing parameters for locating and predicting, the framework can take full advantage of annotated data and enable more potent nonlinear function approximators to enhance model discriminability. Experiments demonstrate state-of-the-art performance for nested named entities[Our codes will be available at: <https://github.com/wuyuefei3/BR>].



There are no comments yet.


page 3

page 4

page 5

page 6

page 7

page 8

page 9

page 10


NEREL: A Russian Dataset with Nested Named Entities, Relations and Events

In this paper, we present NEREL, a Russian dataset for named entity reco...

Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition ...

Deep Cropping via Attention Box Prediction and Aesthetics Assessment

We model the photo cropping problem as a cascade of attention box regres...

Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss

Boundary discontinuity and its inconsistency to the final detection metr...

Bounding Box Tightness Prior for Weakly Supervised Image Segmentation

This paper presents a weakly supervised image segmentation method that a...

3DCarRecog: Car Recognition Using 3D Bounding Box

We present a novel learning framework for vehicle recognition from a sin...

SIMILARnet: Simultaneous Intelligent Localization and Recognition Network

Global Average Pooling (GAP) [4] has been used previously to generate cl...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.