【摘 要】
：
In the last decades,named entity recognition has been extensivelystudied with various supervised learning approaches depend on massive labeled data.In this paper,we focus on person name recognition in
【机 构】
：
Natural Language Processing Lab,School of Computer Science and Technology,Soochow University,China
【出 处】
：
第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会
论文部分内容阅读
In the last decades,named entity recognition has been extensivelystudied with various supervised learning approaches depend on massive labeled data.In this paper,we focus on person name recognition in judgment documents.Owingto thelackofhuman-annotated data,weproposeajoint learningapproach,namely Aux-LSTM,to use a large scale of auto-annotated data to help human-annotated data(in a small size)for person name recognition.Specifically,our approach first develops an auxiliary Long Short-Term Memory(LSTM)repre-sentation by training the auto-annotated data and then leverages the auxiliary LSTM representation toboosttheperformanceofclassifiertrained on thehuman-annotated data.Empirical studies demonstrate the effectiveness of our proposed approach to person name recognition in judgment documents with both human-annotated and auto-annotated data.
其他文献
文本情绪原因识别作为一个新型的研究方向在文本情绪分析领域占据重要地位.本文结合卷积神经网络,提出了一种基于集成卷积神经网络的情绪原因识别方法.该方法通过词向量、卷积、池化等操作充分融合了句子的语义信息,利用多个CNN集成降低数据不平衡性对情绪原因识别的影响,克服了传统情绪原因识别方法的繁琐规则制定、特征抽取、特征空间降维等过程.实验结果表明,本文的方法在情绪原因识别方面取得了较好的效果,对于情绪归
标注《文心雕龙》的篇章结构,据此研究其连接词的显隐、语义及用法.研究发现:1)隐式关系(78.1％)多于显式关系(21.9％),17类关系仅有4类(因果、转折、假设、目的)显多隐少;2)各类关系的同义连接词种数与使用有差异,其中种数最多17(顺承),最少则无(总分、背景);3)连接词(56种)单义为多(44),多义为少(12),义项最多为5,分布有差异.最后,个案分析同义连接词与多义连接词的用法,
To discover semantically coherent topics from topic models,knowledge-based topic models have been proposed to incorporate prior knowledge into topic models.Moreover,some researchers propose life-long
Local community detection is an important research focus in social network analysis.Most existing methods share the intrinsic limitation of utiliz-ing undirected and unweighted networks.In this paper,
随着互联网的发展及硬件的更新,神经网络模型被广泛应用于自然语言处理、图像识别等领域.目前,结合传统自然语言处理方法和神经网络模型正日益成为研究的热点.引入先验知识代表了传统方法的惯例,然而它们对基于神经网络模型的自然语言处理任务的影响尚不清楚.鉴于此,本文尝试探究语言层先验知识对基于神经网络模型的若干自然语言处理任务的影响.根据不同任务的特点,比较了不同先验知识和不同输入位置对不同神经网络模型的影
We take the generation of Chinese classical poetry as a sequence-to-sequence learning problem,and investigate the suitability of recurrent neural network(RNN)for poetry generation task by various qual
Understanding chemical-disease relations(CDR)from biomedicalliterature is important for biomedical research and chemical discovery.This pa-per uses a k-max pooling convolutional neural network(CNN)to
Most state-of-the-art models for named entity recognition(NER)rely on recurrent neural networks(RNNs),in particular long short-term memory(LSTM).Those models learn local and global fea-tures automatic
Word deletion(WD)errors can lead to poor comprehension of the meaning of source translated sentences in phrase-based statistical machine translation(SMT),and have a critical impact on the adequacy of
Answer selection is a crucial subtask of the open domain question answering problem.In this paper,we introduce the Bi-directional Gated Memory Network(BGMN)to model the interactions between question a