A Chinese Named Entity Recognition System with Neural Networks
نویسندگان
چکیده
منابع مشابه
Biomedical Named Entity Recognition Using Neural Networks
We investigate the task of Named Entity Recognition (NER) in the domain of biomedical text. There is little published work employing modern neural network techniques in this domain, probably due to the small sizes of human-labeled data sets, as non-trivial neural models would have great difficulty avoiding overfitting. In this work we follow a semi-supervised learning approach: We first train s...
متن کاملChinese Named Entity Recognition with Multiple Features
This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of each other. Secondly, in order to reduce the searching spac...
متن کاملTransfer Learning for Named-Entity Recognition with Neural Networks
Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, w...
متن کاملNamed Entity Recognition with Gated Convolutional Neural Networks
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 features automatically by RNNs so that hand-craft features can be discarded, totally or partly. Recently, convolutional neural networks (CNNs) have achieved great success on computer vision. However, for NER pr...
متن کاملA Three-Phase System for Chinese Named Entity Recognition
The handling of out-of-vocabulary (OOV) words is one of the key points to a high performance lexical analysis in natural language processing. Among all OOV words, named entities (NE) are the most productive ones. They generally constitute the most meaningful parts of sentences (persons, affairs, time, places, and objects). In this paper, we propose a three-phase “generation, filtering, and reco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ITM Web of Conferences
سال: 2017
ISSN: 2271-2097
DOI: 10.1051/itmconf/20171204002