scholarly journals Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

2020 ◽  
Author(s):  
Takuma Kato ◽  
Kaori Abe ◽  
Hiroki Ouchi ◽  
Shumpei Miyawaki ◽  
Jun Suzuki ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Fine Grained ◽  
Named Entity ◽  
Sequence Labeling

scholarly journals K-best max-margin approaches for sequence labeling

2015 ◽  
Vol 12 (2) ◽  
pp. 465-486
Author(s):  
Dejan Mancev ◽  
Branimir Todorovic
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Computational Time ◽  
Structured Learning ◽  
Training Procedure ◽  
Named Entity ◽  
Sequence Labeling ◽  
Small Collection ◽  
Shallow Parsing ◽  
Output Space

Structured learning algorithms usually require inference during the training procedure. Due to their exponential size of output space, the parameter update is performed only on a relatively small collection built from the ?best? structures. The k-best MIRA is an example of an online algorithm which seeks optimal parameters by making updates on k structures with the highest score at a time. Following the idea of using k-best structures during the learning process, in this paper we introduce four new k-best extensions of max-margin structured algorithms. We discuss their properties and connection, and evaluate all algorithms on two sequence labeling problems, the shallow parsing and named entity recognition. The experiments show how the proposed algorithms are affected by the changes of k in terms of the F-measure and computational time, and that the proposed algorithms can improve results in comparison to the single best case. Moreover, the restriction to the single best case produces a comparison of the existing algorithms.

scholarly journals Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

2021 ◽  
Author(s):  
Xin Zhang ◽  
Guangwei Xu ◽  
Yueheng Sun ◽  
Meishan Zhang ◽  
Pengjun Xie
Keyword(s):  
Domain Adaptation ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

scholarly journals Learning Task-Specific Representation for Novel Words in Sequence Labeling

2019 ◽  
Author(s):  
Minlong Peng ◽  
Qi Zhang ◽  
Xiaoyu Xing ◽  
Tao Gui ◽  
Jinlan Fu ◽  
...  
Keyword(s):  
Empirical Studies ◽  
Named Entity Recognition ◽  
Learning Task ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Word Representation

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e.g., character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.

scholarly journals Fine-grained Dutch named entity recognition

2013 ◽  
Vol 48 (2) ◽  
pp. 307-343 ◽  
Author(s):  
Bart Desmet ◽  
Véronique Hoste
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Fine Grained ◽  
Named Entity

scholarly journals Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1986
Author(s):  
Liguo Yao ◽  
Haisong Huang ◽  
Kuan-Wei Wang ◽  
Shih-Huan Chen ◽  
Qiaoqiao Xiong
Keyword(s):  
Active Learning ◽  
Manufacturing Industry ◽  
Learning Strategy ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Utilization Rate ◽  
Data Types ◽  
Fine Grained ◽  
Named Entity ◽  
Model Transfer

Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.

scholarly journals Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1001 ◽  
Author(s):  
Jingang Liu ◽  
Chunhe Xia ◽  
Haihua Yan ◽  
Wenjing Xu
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Training Model ◽  
Entity Recognition ◽  
Coarse Grained ◽  
Neural Network Modeling ◽  
Fine Grained ◽  
Named Entity

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks.

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