Search Results for author: Junsheng Zhou

Found 17 papers, 3 papers with code

中文词语离合现象识别研究(Research on Recognition of the Separation and Reunion Phenomena of Words in Chinese)

no code implementations CCL 2021 Lou Zhou, Weiguang Qu, Tingxin Wei, Junsheng Zhou, Bin Li, Yanhui Gu

“汉语词语的离合现象是汉语中一种词语可分可合的特殊现象。本文采用字符级序列标注方法解决二字动词离合现象的自动识别问题, 以避免中文分词及词性标注的错误传递, 节省制定匹配规则与特征模板的人工开支。在训练过程中微调BERT中文预训练模型, 获取面向目标任务的字符向量表示, 并引入掩码机制对模型隐藏离用法中分离的词语, 减轻词语本身对识别结果的影响, 强化中间插入成分的学习, 并对前后语素采用不同的掩码以强调其出现顺序, 进而使模型具备了识别复杂及偶发性离用法的能力。为获得含有上下文信息的句子表达, 将原始的句子表达与采用掩码的句子表达分别输入两个不同参数的BiLSTM层进行训练, 最后采用CRF算法捕捉句子标签序列的依赖关系。本文提出的BERT MASK + 2BiLSTMs + CRF模型比现有最优的离合词识别模型提高了2. 85%的F1值。”

An Element-aware Multi-representation Model for Law Article Prediction

no code implementations EMNLP 2020 Huilin Zhong, Junsheng Zhou, Weiguang Qu, Yunfei Long, Yanhui Gu

To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations.

中文连动句语义关系识别研究(Research on Semantic Relation Recognition of Chinese Serial-verb Sentences)

no code implementations CCL 2021 Chao Sun, Weiguang Qu, Tingxin Wei, Yanhui Gu, Bin Li, Junsheng Zhou

“连动句是形如“NP+VP1+VP2”的句子, 句中含有两个或两个以上的动词(或动词结构)且动词的施事为同一对象。相同结构的连动句可以表示多种不同的语义关系。本文基于前人对连动句中VP1和VP2之间的语义关系分类, 标注了连动句语义关系数据集, 基于神经网络完成了对连动句语义关系的识别。该方法将连动句语义识别任务进行分解, 基于BERT进行编码, 利用BiLSTM-CRF先识别出连动句中连动词(VP)及其主语(NP), 再基于融合连动词信息的编码, 利用BiLSTM-Attention对连动词进行关系判别, 实验结果验证了所提方法的有效性。”

基于深度学习的实体关系抽取研究综述(Review of Entity Relation Extraction based on deep learning)

no code implementations CCL 2020 Zhentao Xia, Weiguang Qu, Yanhui Gu, Junsheng Zhou, Bin Li

作为信息抽取的一项核心子任务, 实体关系抽取对于知识图谱、智能问答、语义搜索等自然语言处理应用都十分重要。关系抽取在于从非结构化文本中自动地识别实体之间具有的某种语义关系。该文聚焦句子级别的关系抽取研究, 介绍用于关系抽取的主要数据集并对现有的技术作了阐述, 主要分为:有监督的关系抽取、远程监督的关系抽取和实体关系联合抽取。我们对比用于该任务的各种模型, 分析它们的贡献与缺 陷。最后介绍中文实体关系抽取的研究现状和方法。

Relation Extraction

基于神经网络的连动句识别(Recognition of serial-verb sentences based on Neural Network)

no code implementations CCL 2020 Chao Sun, Weiguang Qu, Tingxin Wei, Yanhui Gu, Bin Li, Junsheng Zhou

连动句是具有连动结构的句子, 是汉语中的特殊句法结构, 在现代汉语中十分常见且使用频繁。连动句语法结构和语义关系都很复杂, 在识别中存在许多问题, 对此本文针对连动句的识别问题进行了研究, 提出了一种基于神经网络的连动句识别方法。本方法分两步:第一步, 运用简单的规则对语料进行预处理;第二步, 用文本分类的思想, 使用BERT编码, 利用多层CNN与BiLSTM模型联合提取特征进行分类, 进而完成连动句识别任务。在人工标注的语料上进行实验, 实验结果达到92. 71%的准确率, F1值为87. 41%。

3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow

1 code implementation29 Mar 2022 Xin Wen, Junsheng Zhou, Yu-Shen Liu, Zhen Dong, Zhizhong Han

Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image.

3D Reconstruction 3D Shape Reconstruction

Self-Supervised Point Cloud Representation Learning with Occlusion Auto-Encoder

1 code implementation26 Mar 2022 Junsheng Zhou, Xin Wen, Yu-Shen Liu, Yi Fang, Zhizhong Han

Our key idea is to randomly occlude some local patches of the input point cloud and establish the supervision via recovering the occluded patches using the remaining visible ones.

Representation Learning

Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments

no code implementations ICCV 2019 Junsheng Zhou, Yuwang Wang, Kaihuai Qin, Wen-Jun Zeng

Our experimental evaluation demonstrates that the result of our method is comparable to fully supervised methods on the NYU Depth V2 benchmark.

Optical Flow Estimation

Unsupervised High-Resolution Depth Learning From Videos With Dual Networks

no code implementations ICCV 2019 Junsheng Zhou, Yuwang Wang, Kaihuai Qin, Wen-Jun Zeng

Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal.

Frame Monocular Depth Estimation

Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing

no code implementations8 Aug 2019 Zhentao Xia, Likai Wang, Weiguang Qu, Junsheng Zhou, Yanhui Gu

In this paper, we describe the details of the neural dependency parser sub-mitted by our team to the NLPCC 2019 Shared Task of Semi-supervised do-main adaptation subtask on Cross-domain Dependency Parsing.

Dependency Parsing Transfer Learning

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