scholarly journals A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign

2019 ◽  
Vol 34 (4) ◽  
pp. 311-321 ◽  
Author(s):  
Minh Quang Nhat Pham
Keyword(s):  
Named Entity Recognition ◽  
Recognition System ◽  
Word Embedding ◽  
Entity Recognition ◽  
Shape Features ◽  
Encoding Scheme ◽  
Named Entity ◽  
Sequence Labeling ◽  
Feature Based ◽  
Word Shape

In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.

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 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.

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