H.3. Artificial Intelligence
Hassan Haji Mohammadi; Alireza Talebpour; Ahamd Mahmoudi Aznaveh; Samaneh Yazdani
Abstract
Coreference resolution is one of the essential tasks of natural languageprocessing. This task identifies all in-text expressions that refer to thesame entity in the real world. Coreference resolution is used in otherfields of natural language processing, such as information extraction,machine translation, ...
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Coreference resolution is one of the essential tasks of natural languageprocessing. This task identifies all in-text expressions that refer to thesame entity in the real world. Coreference resolution is used in otherfields of natural language processing, such as information extraction,machine translation, and question-answering.This article presents a new coreference resolution corpus in Persiannamed Mehr corpus. The article's primary goal is to develop a Persiancoreference corpus that resolves some of the previous Persian corpus'sshortcomings while maintaining a high inter-annotator agreement. Thiscorpus annotates coreference relations for noun phrases, namedentities, pronouns, and nested named entities. Two baseline pronounresolution systems are developed, and the results are reported. Thecorpus size includes 400 documents and about 170k tokens. Corpusannotation is done by WebAnno preprocessing tool.
E. Feli; R. Hosseini; S. Yazdani
Abstract
In Vitro Fertilization (IVF) is one of the scientifically known methods of infertility treatment. This study aimed at improving the performance of predicting the success of IVF using machine learning and its optimization through evolutionary algorithms. The Multilayer Perceptron Neural Network (MLP) ...
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In Vitro Fertilization (IVF) is one of the scientifically known methods of infertility treatment. This study aimed at improving the performance of predicting the success of IVF using machine learning and its optimization through evolutionary algorithms. The Multilayer Perceptron Neural Network (MLP) were proposed to classify the infertility dataset. The Genetic algorithm was used to improve the performance of the Multilayer Perceptron Neural Network model. The proposed model was applied to a dataset including 594 eggs from 94 patients undergoing IVF, of which 318 were of good quality embryos and 276 were of lower quality embryos. For performance evaluation of the MLP model, an ROC curve analysis was conducted, and 10-fold cross-validation performed. The results revealed that this intelligent model has high efficiency with an accuracy of 96% for Multi-layer Perceptron neural network, which is promising compared to counterparts methods.
