Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is laborious to manually design the most informative features for such a system. In this paper, we develop an approach based on recurrent neural networks to learn the relation between an essay and its assigned score, without any feature engineering. We explore several neural network models for the task of automated essay scoring and perform some analysis to get some insights of the models. The results show that our best system, which is based on long short-term memory networks, outperforms a strong baseline by 5.6% in terms of quadratic weighted Kappa, without requiring any feature engineering.
One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity. However, a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy. Since word embeddings are typically obtained from unlabeled data using unsupervised methods, this method can be seen as a semi-supervised word sense disambiguation approach. This paper investigates two ways of incorporating word embeddings in a word sense disambiguation setting and evaluates these two methods on some Sen-sEval/SemEval lexical sample and all-words tasks and also a domain-specific lexical sample task. The obtained results show that such representations consistently improve the accuracy of the selected supervised WSD system. Moreover, our experiments on a domainspecific dataset show that our supervised baseline system beats the best knowledge-based systems by a large margin.
Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.
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