Named-Entity Recognition from Greek and English Texts

Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.     

基于深度学习的命名实体识别综述

命名实体识别是自然语言处理的基础任务之一,目的是从非结构化的文本中识别出所需的实体及类型,其识别的结果可用于实体关系抽取、知识图谱构建等众多实际应用。近些年,随着深度学习在自然语言处理领域的广泛应用,各种基于深度学习的命名实体识别方法均取得了较好的效果,其性能全面超越传统的基于人工特征的方法。该文从三个方面介绍近期基于深度学习的命名实体识别方法: 第一,从输入层、编码层和解码层出发,介绍命名实体识别的一般框架;第二,分析汉语命名实体识别的特点,着重介绍各种融合字词信息的模型;第三,介绍低资源的命名实体识别,主要包括跨语言迁移方法、跨领域迁移方法、跨任务迁移方法和集成自动标注语料的方法等。最后,总结相关工作,并提出未来可能的研究方向。     

基于深度学习的命名实体识别研究综述

命名实体识别技术是信息抽取、机器翻译、问答系统等多种自然语言处理技术中一项重要的基本任务。近年来,基于深度学习的命名实体识别技术成为一大研究热点。为了方便研究者们了解基于深度学习的命名实体识别研究进展及未来发展趋势,对当前基于卷积神经网络、循环神经网络、transformer模型以及其他一些命名实体识别方法展开综述性介绍,对四类方法进行了深入分析和对比。同时对命名实体识别应用领域以及所涉及到的数据集和评测方法进行了介绍,并对未来的研究方向进行了展望。     

Deep learning-based extraction of construction procedural constraints from construction regulations

Construction procedural constraints are critical in facilitating effective construction procedure checking in practice and for various inspection systems. Nowadays, the manual extraction of construction procedural constraints is costly and time-consuming. The automatic extraction of construction procedural constraint knowledge (e.g., knowledge entities and interlinks/relationships between them) from regulatory documents is a key challenge. Traditionally, natural language processing is implemented using either rule-based or machine learning approaches. Limited efforts on rule-based extraction of construction regulations often rely on pre-defined vocabularies and involve heavy feature engineering. Based on characteristics of the knowledge expression of construction procedural constraints in Chinese regulations, this paper explores a hybrid deep neural network, combining the bidirectional long short-term memory (Bi-LSTM) and the conditional random field (CRF), for the automatic extraction of the qualitative construction procedural constraints. Based on the proposed deep neural network, the recognition and extraction of named entities and relations between them are realized. Unlike existing information extraction research efforts using rule-based methods, the proposed hybrid deep learning approach can be applied without complex handcrafted features engineering. Besides, the long distance dependency relationships between different entities in regulations are considered. The model implementation results demonstrate the good performance of the end-to-end deep neural network in the extraction of construction procedural constraints. This study can be considered as one of the early explorations of knowledge extraction from construction regulations.     

Interactive Ambiguity Resolution of Named Entities in Fictional Literature

Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well‐known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post‐processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use‐cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.     

Integrating linguistic knowledge into a conditional random fieldframework to identify biomedical named entities

As new high-throughput technologies have created an explosion of biomedical literature, there arises a pressing need for automatic information extraction from the literature bank. To this end, biomedical named entity recognition (NER) from natural language text is indispensable. Current NER approaches include: dictionary based, rule based, or machine learning based. Since, there is no consolidated nomenclature for most biomedical NEs, any NER system relying on limited dictionaries or rules does not seem to perform satisfactorily. In this paper, we consider a machine learning model, CRF, for the construction of our NER framework. CRF is a well-known model for solving other sequence tagging problems. In our framework, we do our best to utilize available resources including dictionaries, web corpora, and lexical analyzers, and represent them as linguistic features in the CRF model. In the experiment on the JNLPBA 2004 data, with minimal post-processing, our system achieves an F-score of 70.2%, which is better than most state-of-the-art systems. On the GENIA 3.02 corpus, our system achieves an F-score of 78.4% for protein names, which is 2.8% higher than the next-best system. In addition, we also examine the usefulness of each feature in our CRF model. Our experience could be valuable to other researchers working on machine learning based NER.     

融合多特征的最大熵汉语命名实体识别模型

最大熵模型能有效整合多种约束信息,对于汉语命名实体识别具有很好的适用性,因此,将其作为基本框架,提出一种融合多特征的最大熵汉语命名实体识别模型,该模型集成局部与全局多种特征,同时为降低搜索空间并提高处理效率,而引入了启发式知识,基于SIGHAN 2008命名实体评测任务测试数据的实验结果表明,所建立的混合模式是一种组合统计模型与启发式知识的有效汉语命名实体识别模式,基于不同测试数据的实验说明,该方法针对不同测试数据源具有一致性.     

OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Named entity recognition (NER) and relation extraction (RE) are two important subtasks in information extraction (IE). Most of the current learning methods for NER and RE rely on supervised machine learning techniques with more accurate results for NER than RE. This paper presents OntoILPER a system for extracting entity and relation instances from unstructured texts using ontology and inductive logic programming, a symbolic machine learning technique. OntoILPER uses the domain ontology and takes advantage of a higher expressive relational hypothesis space for representing examples whose structure is relevant to IE. It induces extraction rules that subsume examples of entities and relation instances from a specific graph-based model of sentence representation. Furthermore, OntoILPER enables the exploitation of the domain ontology and further background knowledge in the form of relational features. To evaluate OntoILPER, several experiments over the TREC corpus for both NER and RE tasks were conducted and the yielded results demonstrate its effectiveness in both tasks. This paper also provides a comparative assessment among OntoILPER and other NER and RE systems, showing that OntoILPER is very competitive on NER and outperforms the selected systems on RE.     

基于标签聚类的中文重叠命名实体识别方法

为解决命名实体之间的复杂嵌套以及语料库中标注误差导致的相邻命名实体边界重叠问题,提出一种中文重叠命名实体识别方法。利用基于随机合并与拆分的层次化聚类算法将重叠命名实体标签划分到不同的聚类簇中,建立文字到实体标签之间的一对一关联关系,解决了实体标签聚类陷入局部最优的问题,并在每个标签聚类簇中采用融合中文部首的BiLSTM-CRF模型提高重叠命名实体的识别稳定性。实验结果表明,该方法通过标签聚类的方式有效避免标注误差对识别过程的干扰,F1值相比现有识别方法平均提高了0.05。     

基于深度神经网络的法语命名实体识别模型

现有法语命名实体识别（NER）研究中，机器学习模型多使用词的字符形态特征，多语言通用命名实体模型使用字词嵌入代表的语义特征，都没有综合考虑语义、字符形态和语法特征。针对上述不足，设计了一种基于深度神经网络的法语命名实体识别模型CGC-fr。首先从文本中提取单词的词嵌入、字符嵌入和语法特征向量；然后由卷积神经网络（CNN）从单词的字符嵌入序列中提取单词的字符特征；最后通过双向门控循环神经网络（BiGRU）和条件随机场（CRF）分类器根据词嵌入、字符特征和语法特征向量识别出法语文本中的命名实体。实验中，CGC-fr在测试集的F1值能够达到82.16%，相对于机器学习模型NERC-fr、多语言通用的神经网络模型LSTM-CRF和Char attention模型，分别提升了5.67、1.79和1.06个百分点。实验结果表明，融合三种特征的CGC-fr模型比其他模型更具有优势。     

动态迁移实体块信息的跨领域中文实体识别模型

由于中文文本之间没有分隔符,难以识别中文命名实体的边界.此外,在垂直领域中难以获取充足的标记完整的语料,例如医疗领域和金融领域等垂直领域.为解决上述不足,提出一种动态迁移实体块信息的跨领域中文实体识别模型(TES-NER),将跨领域共享的实体块信息(entity span)通过基于门机制(gate mechanism)的动态融合层,从语料充足的通用领域(源领域)动态迁移到垂直领域(目标领域)上的中文命名实体模型,其中,实体块信息用于表示中文命名实体的范围.TES-NER模型首先通过双向长短期记忆神经网络(BiLSTM)和全连接网络(FCN)构建跨领域共享实体块识别模块,用于识别跨领域共享的实体块信息以确定中文命名实体的边界;然后,通过独立的基于字的双向长短期记忆神经网络和条件随机场(BiLSTM-CRF)构建中文命名实体识别模块,用于识别领域指定的中文命名实体;最后构建动态融合层,将实体块识别模块抽取得到的跨领域共享实体块信息通过门机制动态决定迁移到领域指定的命名实体识别模型上的量.设置通用领域(源领域)数据集为标记语料充足的新闻领域数据集(MSRA),垂直领域(目标领域)数据集为混合领域(OntoNotes 5.0)、金融领域(Resume)和医学领域(CCKS 2017)这3个数据集,其中,混合领域数据集(OntoNotes 5.0)是融合了6个不同垂直领域的数据集.实验结果表明,提出的模型在OntoNotes 5.0、Resume和CCKS 2017这3个垂直领域数据集上的F1值相比于双向长短期记忆和条件随机场模型(BiLSTM-CRF)分别高出2.18%、1.68%和0.99%.     

中文领域命名实体识别综述

命名实体识别（Named Entity Recognition,NER）作为自然语言处理领域经典的研究主题,是智能问答、知识图谱等任务的基础技术。领域命名实体识别（Domain Named Entity Recognition,DNER）是面向特定领域的NER方案。在深度学习技术的推动下,中文DNER取得了突破性进展。概括了中文DNER的研究框架,从领域数据源的确定、领域实体类型及规范制定、领域数据集的标注规范、中文DNER评估指标四个角度对国内外已有研究成果进行了综合评述;总结了目前常见的中文DNER的技术框架,介绍了基于词典和规则的模式匹配方法、统计机器学习方法、基于深度学习的方法、多方融合的深度学习方法,并重点分析了基于词向量表征和深度学习的中文DNER方法;讨论了中文DNER的典型应用场景,对未来发展方向进行了展望。     

Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model

As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level features encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.     

基于上下文相关字向量的中文命名实体识别

命名实体识别(NER)旨在识别出文本中的专有名词,并对其进行分类。由于用于监督学习的训练数据通常由人工标注,耗时耗力,因此很难得到大规模的标注数据。为解决中文命名实体识别任务中因缺乏大规模标注语料而造成的数据稀缺问题,以及传统字向量不能解决的一字多义问题,文中使用在大规模无监督数据上预训练的基于上下文相关的字向量,即利用语言模型生成上下文相关字向量以改进中文NER模型的性能。同时,为解决命名实体识别中的未登录词问题,文中提出了基于字语言模型的中文NER系统。把语言模型学习到的字向量作为NER模型的输入,使得同一中文汉字在不同语境中有不同的表示。文中在6个中文NER数据集上进行了实验。实验结果表明,基于上下文相关的字向量可以很好地提升NER模型的性能,其平均性能F1值提升了4.95%。对实验结果进行进一步分析发现,新系统在OOV实体识别上也可以取得很好的效果,同时对一些特殊类型的中文实体识别也有不错的表现。     

基于 Deep Belief Nets 的中文名实体关系抽取

关系抽取是信息抽取的一项子任务,用以识别文本中实体之间的语义关系.提出一种利用DBN(deepbelief nets)模型进行基于特征的实体关系抽取方法,该模型是由多层无监督的RBM(restricted Boltzmann machine)网络和一层有监督的BP(back-propagation)网络组成的神经网络分类器.RBM网络以确保特征向量映射达到最优,最后一层BP网络分类RBM网络的输出特征向量,从而训练实体关系分类器.在ACE04语料上进行的相关测试,一方面证明了字特征比词特征更适用于中文关系抽取任务;另一方面设计了3组不同的实验,分别使用正确的实体类别信息、通过实体类型分类器得到实体类型信息和不使用实体类型信息,用以比较实体类型信息对关系抽取效果的影响.实验结果表明,DBN非常适用于基于高维空间特征的信息抽取任务,获得的效果比SVM和反向传播网络更好.     

跨语言命名实体翻译对抽取的研究综述

跨语言命名实体对于机器翻译、跨语言信息抽取都具有重要意义,从命名实体的音译、基于平行/可比语料库的跨语言命名实体对齐、基于网络挖掘的跨语言命名实体对翻译抽取3个方面对跨语言命名实体翻译对抽取的研究现状进行了总结。音译是跨语言命名实体翻译对抽取的重点内容之一,基于深度学习的音译模型将是今后的研究重点。目前,跨语言平行/可比语料库的获取和标注直接影响基于语料库的跨语言命名实体对齐的深入研究。基于信息检索和维基百科的跨语言命名实体翻译对抽取研究将是跨语言命名实体翻译对抽取研究的趋势。     

ArRaNER: A novel named entity recognition model for biomedical literature documents

Developments in advanced innovations have prompted the generation of an immense amount of digital information. The data deluge contains hidden information that is difficult to extract. In the biomedical domain, the development of technology has caused the production of voluminous data. Processing these voluminous textual data is referred to as ‘biomedical content mining’. Emerging artificial intelligence (AI) models play a major role in the automation of Pharma 4.0. In AI, natural language processing (NLP) plays a dynamic role in extracting knowledge from biomedical documents. Research articles published by scientists and researchers contain an enormous amount of hidden information. Most of the original and peer-reviewed articles are indexed in PubMed. Extracting meaningful information from a large number of literature documents is very difficult for human beings. This research aims to extract the named entities of literature documents available in the life science domain. A high-level architecture is proposed along with a novel named entity recognition (NER) model. The model is built using rule-based machine learning (ML). The proposed ArRaNER model produced better accuracy and was also able to identify more entities. The NER model was tested on two different datasets: a PubMed dataset and a Wikipedia talk dataset. The ArRaNER model obtains an accuracy of 83.42% on the PubMed articles and 77.65% on the Wikipedia articles.

     

Product named entity recognition in Chinese text

There are many expressive and structural differences between product names and general named entities such as person names, location names and organization names. To date, there has been little research on product named entity recognition (NER), which is crucial and valuable for information extraction in the field of market intelligence. This paper focuses on product NER (PRO NER) in Chinese text. First, we describe our efforts on data annotation, including well-defined specifications, data analysis and development of a corpus with annotated product named entities. Second, a hierarchical hidden Markov model-based approach to PRO NER is proposed and evaluated. Extensive experiments show that the proposed method outperforms the cascaded maximum entropy model and obtains promising results on the data sets of two different electronic product domains (digital and cell phone).

基于深度学习的医疗命名实体识别

医疗命名实体识别指从海量的非结构化的医疗数据中提取关键信息,为医学研究的发展和智慧医疗系统的普及提供了基础.深度学习运用深层非线性的神经网络结构能够学习到复杂、抽象的特征,可实现对数据更本质的表征.医疗命名实体识别采用深度学习模型可明显提升效果.首先,本文综述了医疗命名实体识别特有的难点以及传统的识别方法;其次,总结了基于深度学习方法的模型并介绍了较为流行的模型改进方法,包括针对特征向量的改进,针对数据匮乏、复杂命名实体识别等问题的改进;最后,通过综合论述对未来的研究方向进行展望.     

不规则文本中商品名称识别的特征选择

传统的命名实体识别任务多见于人名、地名、机构名这些普通的命名实体,且大多采用规则文本进行研究。随着电子商务和互联网广告的不断发展,如何从用户的各种不规则的上下文信息中自动识别出商品名称这一特殊的命名实体成为了一个需要解决的问题。为了解决这一问题,建立了一个最大熵模型用于识别论坛发帖这种不规则文本中的商品名称,并探讨了多种特征对于识别效果的影响。这些特征不仅包括传统命名实体识别方法中所使用的局部特征和布朗聚类特征,还包括词的分布式表示这种比较新颖的特征。这些特征按照各种不同的方式进行组合作为模型的输入。在CPROD01评测数据集上的实验结果表明,布朗聚类特征能够有效地提高商品名称识别系统的准确性。