scholarly journals GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

2017 ◽  
Vol 34 (9) ◽  
pp. 1547-1554 ◽  
Author(s):  
Qile Zhu ◽  
Xiaolin Li ◽  
Ana Conesa ◽  
Cécile Pereira
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Local Context ◽  
Learning Approach ◽  
Biomedical Text ◽  
Named Entity

scholarly journals Anyuak Language Named Entity Recognition Using Deep Learning Approach

2021 ◽  
Vol 14 (39) ◽  
pp. 2998-3006
Author(s):  
Birhanu Gardie ◽  
◽  
Smegnew Asemie ◽  
Kassahun Azezew
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approach ◽  
Named Entity

scholarly journals Arabic named entity recognition using deep learning approach

2019 ◽  
Vol 9 (3) ◽  
pp. 2025 ◽  
Author(s):  
Ismail El Bazi ◽  
Nabil Laachfoubi
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Short Term Memory ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Feature Engineering ◽  
Learning Approach ◽  
Sources Of Information ◽  
Named Entity

Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.

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.

scholarly journals A hybrid deep-learning approach for complex biochemical named entity recognition

2021 ◽  
pp. 106958
Author(s):  
Jian Liu ◽  
Lei Gao ◽  
Sujie Guo ◽  
Rui Ding ◽  
Xin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approach ◽  
Named Entity

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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