Chinese Named Entity Recognition with a Sequence Labeling Approach: Based on Characters, or Based on Words?

2010 ◽  
pp. 634-640 ◽  
Author(s):  
Zhangxun Liu ◽  
Conghui Zhu ◽  
Tiejun Zhao
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Sequence Labeling ◽  
Labeling Approach

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.

scholarly journals Indonesian Sentence Boundary Detection using Deep Learning Approaches

2021 ◽  
Vol 4 (1) ◽  
pp. 38
Author(s):  
Joan Santoso ◽  
Esther Irawati Setiawan ◽  
Christian Nathaniel Purwanto ◽  
Fachrul Kurniawan
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approaches ◽  
Named Entity ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Sentence Patterns ◽  
Sentence Boundary

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes..

scholarly journals Improving Distantly-Supervised Named Entity Recognition for Traditional Chinese Medicine Text via a Novel Back-Labeling Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145413-145421
Author(s):  
Dezheng Zhang ◽  
Chao Xia ◽  
Cong Xu ◽  
Qi Jia ◽  
Shibing Yang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Labeling Approach

scholarly journals Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

2017 ◽  
Vol 5 ◽  
pp. 247-261 ◽  
Author(s):  
Gáabor Berend
Keyword(s):  
Sparse Coding ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Proposed Model

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e. 150 sentences per language.

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 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 Improving deep learning method for biomedical named entity recognition by using entity definition information

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Ying Xiong ◽  
Shuai Chen ◽  
Buzhou Tang ◽  
Qingcai Chen ◽  
Xiaolong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
The State ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Learning Methods ◽  
Named Entity ◽  
Sequence Labeling ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information. Material and method We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score. Results Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER. Conclusion Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

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