A deep learning-based bilingual Hindi and Punjabi named entity recognition system using enhanced word embeddings

2021 ◽  
pp. 107601
Author(s):  
Archana Goyal ◽  
Vishal Gupta ◽  
Manish Kumar
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Recognition System ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity

scholarly journals Deep learning with word embeddings improves biomedical named entity recognition

2017 ◽  
Vol 33 (14) ◽  
pp. i37-i48 ◽  
Author(s):  
Maryam Habibi ◽  
Leon Weber ◽  
Mariana Neves ◽  
David Luis Wiegandt ◽  
Ulf Leser
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity ◽  
Biomedical Named Entity Recognition

scholarly journals Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

2020 ◽  
Author(s):  
Liliya Akhtyamova ◽  
Paloma Martínez ◽  
Karin Verspoor ◽  
John Cardiff
Keyword(s):  
Deep Learning ◽  
Clinical Case ◽  
Named Entity Recognition ◽  
Performance Measure ◽  
Spanish Language ◽  
Entity Recognition ◽  
Superior Performance ◽  
Word Embeddings ◽  
Named Entity ◽  
Radiology Reports

Abstract Background: In the Big Data era there is an increasing need to fully exploit and analyse the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute to extract relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patient’s discharge summaries and radiology reports among others. Extracted information could help in health-related decision making processes. Named entity recognition (NER) devoted to detect important concepts in texts (diseases, symptoms, drugs, etc.) is a crucial task in information extraction processes especially in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narrative. Methods: We explore the use of contextualized word embeddings to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections derived from open access Spanish-language medical publications. Results: NER system integrates in-domain pre-trained Flair and FastText word embeddings, byte-pairwise encoded and the bi-LSTM-based character word embeddings. The system yielded the best performance measure with F-score of 90.84%. Error analysis showed that the main source of errors for the best model is the newly detected false positive entities with the half of that amount of errors belonged to longer than the actual ones detected entities. Conclusions: Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over the system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.

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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