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

DEVELOPMENT OF A STRATEGY FOR QUANTIFYING THE IMPACT OF ODOROUS EMISSIONS FROM STATIONARY SOURCES ON THE SURROUNDING COMMUNITIES.

Regulatory agencies are expected to deal routinely with community odor problems yet they have no objective methods for assessing the effects of odorous sources. A three step strategy has been developed for quantifying the impacts of existing or proposed stationary odorous sources on their surrounding communities. Successful implementation of the proposed protocols would establish: (1) whether there is a recognizable odor problem in the community; (2) how bad the odor is; (3) how much odor there is. A public attitude survey has been designed to aid in the confirmation of recognizable odor problems in the community. Quantification of odors with respect to how bad is done through an evaluation of the degree of offensiveness (DO) as the product of: (1) intensity; expressed as maximum dilution level at 100 percent probability of complaint..

Prevalence of peripapillary choroidal neovascular membranes (PPCNV) in an elderly UK population—the Bridlington eye assessment project (BEAP): a cross-sectional study (2002–2006)

© 2018, The Royal College of Ophthalmologists. Purpose: There is paucity of data on the epidemiology of peripapillary choroidal neovascularisartion (PPCNV). Our aim was to determine prevalence of PPCNV in the elderly UK population of Bridlington residents aged ≥65 years. Methods: Eyes with PPCNV in the Bridlington eye assessment project (BEAP) database of 3475 participants were analysed. PPCNV outline was drawn, its area measured, and clock-hour involvement of disc circumference recorded. Location and shortest distance from the lesion edge to fovea were recorded. Masked grading for age-related maculopathy (ARM)/reticular pseudodrusen (RPD) within the ETDRS grid was assigned for each eye using a modified Rotterdam scale. Peripapillary retinal pigment epithelial (RPE) changes/drusen were recorded. Visual acuity (VA) and demographic details analysed separately were merged with grading data. Results: PPCNV were identified in ten subjects, and were bilateral in two (20%), a population prevalence of 0.29%, and 0.06% bilaterality. Gender-specific prevalence were 0.36% and 0.19% for females and males, respectively. Age ranged from 66 to 85 years [mean 76.3 (SD 6.4)]. PPCNV were located nasal to disc in 41.7%, measuring 0.46–7.93 mm 2 [mean 2.81 mm 2 (SD 2.82)]. All PPCNV eyes had peripapillary RPE changes. One subject had no ARM, 1 angioid streaks, and 30% RPD. No direct foveal involvement, or reduced VA attributable to PPCNV was observed. Conclusion: PPCNV were infrequent in this population, more common in females, and often located nasal to the disc, without foveal extension. Peripapillary degenerative changes were universal, and strong association with ARM was observed in eyes with PPCNV. Typically, PPCNV were asymptomatic with VA preservation

Prevalence of optic disc haemorrhages in an elderly UK Caucasian population and possible association with reticular pseudodrusen—the Bridlington Eye Assessment Project (BEAP): a cross-sectional study (2002–2006)

Aims: To determine disc haemorrhages (DH) prevalence in an elderly UK population-the Bridlington Eye Assessment Project (BEAP).Methods: Thirty-degree (30°) fundus photographs (3549 participants ≥65 years) were graded for DH/macula changes. Glaucoma evaluation included Goldmann tonometry, 26-point suprathreshold visual-fields and mydriatic slit-lamp assessment for glaucomatous optic neuropathy.Results: 3548 participants with photographs in at least one eye. DH were present in 53 subjects (1.49%), increasing from 1.17% (65-69-year age-group) to 2.19% (80-84-year age53 group), p=0.06. DH was found in 9/96 (9.38%) right eyes (RE) with open angle glaucoma (OAG). Two of twelve RE (16.67%) with normal tension glaucoma (NTG) had DH. Prevalence in eyes without glaucoma was lower (32/3452, [0.93%]). Reticular pseudodrusen (RPD) occurred in 170/3212 (5.29%) subjects without DH, and 8/131 subjects (6.11%) with OAG. Twenty (20) eyes had normal tension glaucoma (NTG), 2 of whom had RPD (10%) (p=0.264). Within a logistic regression model, DH was associated with glaucoma (OR 10.2, 95% CI 5.32 - 19.72) and increasing age (OR 1.05, 95% CI 1.00-1.10, p=0.03). DH was associated with RPD (p=0.05) with univariate analysis but this was not statistically significant in the final adjusted model. There was no significant association with gender, diabetes mellitus (DM), hypertension treatment or AMD grade.Conclusion: DH prevalence is 1.5% in those over 65 years old and significantly associated with glaucoma and increasing age. There appears to be increased RPD prevalence in eyes with DH and NTG with age acting as a confounding factor. Larger studies are required to fully assess the relationship and investigate a possible shared aetiology of choroidal ischaemia

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

Image analysis and machine learning for detecting malaria

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis.We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images.We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis

Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose