Chinese Named Entity Recognition based on Conditional Random Fields

命名实体识别属于自然语言处理的基础研究领域，是信息抽取、信息检索、机器翻译、组块分析、问答系统等多种自然语言处理技术的重要基础。因此，对命名实体识别的研究具有很大的实用意义。本文针对现代汉语文本的特点，主要研究以人名、地名和组织名的识别为核心内容的中文命名实体识别问题，我们以一种较新型的统计模型--条件随机场为基本框架，设计并实现了一个中文命名实体识别系统。具体说来，本文的主要内容如下：本文首先分析了命名实体识别的难点，人名、地名、组织名的相关语言学知识，并对现有的一些命名实体识别方法和中文命名实体识别系统进行了简要介绍。接着，详细介绍了条件随机场的定义、模型结构、势函数、参数估计和训练方法、...Named entity recognition is one of the fundamental problems in many natural language processing applications, such as information extraction, information retrieval, machine translation, shallow parsing and question answering system. The research of named entity recognition is of great worth. According to the modern Chinese characteristics, this paper mainly researches Chinese named entity recog...学位：工学硕士院系专业：计算机与信息工程学院计算机科学系_计算机应用技术学号：20032802

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V

IRISA System for Entity Detection and Linking at CLEF HIPE 2020

International audienceThis note describes IRISA's system for the task of named entity processing on historical newspapers in French. Following a standard entity detection and linking pipeline, our system implements three steps to solve the named entity linking task. Named Entity Recognition (NER) is first performed to identify the entity mentions in a document based on a Conditional Random Fields classifier. Candidate entities from Wikidata are then generated for each mention found, using simple search. Finally, every mention is linked to one of its candidate entities in a so-called linking step leveraging various string metrics and the semantic structure of Wikidata to improve on the linking decisions