PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

Clinical risk stratification in glaucoma

Glaucoma is the leading cause of preventable sight loss in the United Kingdom and the provision of timely glaucoma care has been highlighted as a significant challenge in recent years. Following a recent high-profile investigation, The Healthcare Safety Investigation Branch recommended the validation of risk stratification models to safeguard the vision-related quality of life of glaucoma patients. There continues to be no nationally agreed evidence-based risk stratification model for glaucoma care across the United Kingdom. Some models have used simple measures of disease staging such as visual field mean deviation as surrogates for risk, but more refined, individualised risk stratification models should include factors related to both visual impairment and visual disability. Candidate tools should also incorporate both ocular and systemic co-morbidities, rate of disease progression, visual needs and driving status and undergo clinical refinement and validation to justify implementation. The disruption to routine glaucoma care caused by the COVID-19 pandemic has only highlighted the importance of such risk stratification models and has accelerated their development, application and evaluation. This review aims to critically appraise the available evidence underpinning current approaches for glaucoma risk stratification and to discuss how these may be applied to contemporary glaucoma care within the United Kingdom. Further research will be essential to justify and validate the utility of glaucoma risk stratification models in everyday clinical practice

Cluster Labeling by Word Embeddings and WordNet’s Hypernymy

Cluster labeling is the assignment of representative labels to clusters of documents or words. Once assigned, the labels can play an important role in applications such as navigation, search and document classification. However, finding appropriately descriptive labels is still a challenging task. In this paper, we propose various approaches for assigning labels to word clusters by leveraging word embeddings and the synonymy and hypernymy relations in the WordNet lexical ontology. Experiments carried out using the WebAP document dataset have shown that one of the approaches stand out in the comparison and is capable of selecting labels that are reasonably aligned with those chosen by a pool of four human annotators

BILSTM-CRF for Persian named-entity recognition armanpersonercorpus: The first entity-annotated Persian dataset

© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved. Named-entity recognition (NER) can still be regarded as work in progress for a number of Asian languages due to the scarcity of annotated corpora. For this reason, with this paper we publicly release an entity-annotated Persian dataset and we present a performing approach for Persian NER based on a deep learning architecture. In addition to the entity-annotated dataset, we release a number of word embeddings (including GloVe, skip-gram, CBOW and Hellinger PCA) trained on a sizable collation of Persian text. The combination of the deep learning architecture (a BiLSTM-CRF) and the pre-trained word embeddings has allowed us to achieve a 77.45% CoNLL F1 score, a result that is more than 12 percentage points higher than the best previous result and interesting in absolute terms

Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears

Objective: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results are obtained for parasite detection in thick blood smears for a smartphone application using deep learning methods. Significance: Automated parasite detection running on smartphones is a promising alternative to manual parasite counting for malaria diagnosis, especially in areas lacking experienced parasitologists.</br