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Automatic Text Scoring Using Neural Networks

Dimitrios Alikaniotis, Helen Yannakoudakis, Marek Rei
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches.  ...  In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model  ...  Then, we can assess the 'goodness' of this embedding by evaluating the error gradients after predicting the highest/lowest score.  ... 
doi:10.18653/v1/p16-1068 dblp:conf/acl/AlikaniotisYR16 fatcat:722xmf6erbecdlgcyl4j3deqcu

Automatic Text Scoring Using Neural Networks [article]

Dimitrios Alikaniotis, Helen Yannakoudakis, Marek Rei, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository
2016
Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches.  ...  In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model  ...  Then, we can assess the 'goodness' of this embedding by evaluating the error gradients after predicting the highest/lowest score.  ... 
doi:10.17863/cam.376 fatcat:rdtcne3tpbatzn2f4z6assetdy

August 2016 VOLUME 5, ISSUE 8, AUGUST 2016 5th Generation Wi-Fi Shatha Ghazal, Raina S Alkhlailah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5801 ECG Arrhythmia Detection Using Choi-Williams Time-Frequency Distribution and Artificial Neural Network Sanjit K. Dash, G. Sasibhushana Rao Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5802 Data Security using RSA Algorithm in Cloud Computing Santosh Kumar Singh, Dr. P.K. Manjhi, Dr. R.K. Tiwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2 ...

Abdullah K K A, Robert A B C, Adeyemo A B
2016 IJARCCE  
This paper discussed query terms with semantic search for retrieval of web content using different semantic indexing techniques.  ...  To address this issue, queries need to be disambiguated by considering the context (concept) using semantic search terms to index the search engine.  ...  Latent Semantic Indexing (LSI) exploits the use of statistical relation to determine the semantically relevant content as long as the relations are generated automatically from document.  ... 
doi:10.17148/ijarcce.2016.5869 fatcat:ihbu5ekllnhqjbuw6yeetnin3q

Automatic Scoring of Spoken Language Based on Basic Deep Learning

Zhong Cheng, Zonghua Wang, Hangjun Che
2022 Scientific Programming  
Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network.  ...  Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English.  ...  When using neural network to solve text problems, the network architecture shown in Figure 4 is usually used. e first layer of the network is the word embedding layer, which transforms the words in the  ... 
doi:10.1155/2022/6884637 fatcat:6vg6nkqmt5fojiz45bfur7bo3q

Fully automatic scoring of handwritten descriptive answers in Japanese language tests [article]

Hung Tuan Nguyen, Cuong Tuan Nguyen, Haruki Oka, Tsunenori Ishioka, Masaki Nakagawa
2022 arXiv   pre-print
We present our attempt to adapt deep neural network-based handwriting recognizers trained on a labelled handwriting dataset into this unlabeled answer set.  ...  As QWK is over 0.8, it represents acceptable similarity of scoring between the automatic scoring system and the human examiners.  ...  Thus, automatic scoring is considered as text classification in this paper. We use BERT [16] , which is pre-trained on Japanese Wikipedia.  ... 
arXiv:2201.03215v1 fatcat:huavc5bd7nh3rgqgdqhineavgq

Automatic Integrated Scoring Model for English Composition Oriented to Part-Of-Speech Tagging

Fei Chen
2021 Complexity  
Then, the features of the text content are extracted, and the automatic scoring model of English composition is constructed by means of model fusion.  ...  First, use the convolutional neural network to extract the word information from the character level and use this part of the information in the coarse-grained learning layer.  ...  When using neural networks to process text data, you first need to digitize or vectorize words. Many network structures map words into a data vector.  ... 
doi:10.1155/2021/5544257 doaj:237dcabe6ba84eca83deeb4ee79060ed fatcat:nsvtt22hqrgkbhpwcq2eiihhhq

Research and Design of Automatic Scoring Algorithm for English Composition Based on Machine Learning

Yu Zhao, Baiyuan Ding
2021 Scientific Programming  
However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms.  ...  Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm  ...  [24] proposed a combination model of deep neural network DC-NN, which used an improved recursive neural network to generate phrase pair semantic vectors suitable for phrase generation process and used  ... 
doi:10.1155/2021/3429463 fatcat:ux6o6rvrsrhspnrcjcgbwycefa

Automated language essay scoring systems: a literature review

Mohamed Abdellatif Hussein, Hesham Hassan, Mohammad Nassef
2019 PeerJ Computer Science  
Automated Essay Scoring (AES) systems are used to overcome the challenges of scoring writing tasks by using Natural Language Processing (NLP) and machine learning techniques.  ...  Methodology We have reviewed the existing literature using Google Scholar, EBSCO and ERIC to search for the terms "AES", "Automated Essay Scoring", "Automated Essay Grading", or "Automatic Essay" for essays  ...  Automatic featuring AES systems Automatic text scoring using neural networks Alikaniotis, Yannakoudakis, and Rei introduced in 2016 a deep neural network model capable of learning features automatically  ... 
doi:10.7717/peerj-cs.208 pmid:33816861 pmcid:PMC7924549 fatcat:27zku2x4rrfa5kmiz2c47hglgi

Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha
2018 Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications  
In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task.  ...  features associated within a text document along with a hierarchical convolution recurrent neural network framework.  ...  Both recurrent neural networks (Williams and Zipser, 1989; Mikolov et al., 2010) and convolution neural networks (LeCun et al., 1998; Kim, 2014) have been used to automatically score input essays.  ... 
doi:10.18653/v1/w18-3713 dblp:conf/acl-tea/DasguptaNDS18 fatcat:ua5xzitnkre4patbvbxo5jo6gi

G-Rap: interactive text synthesis using recurrent neural network suggestions

Udo Schlegel, Eren Cakmak, Juri Buchmüller, Daniel A. Keim
2018 The European Symposium on Artificial Neural Networks  
Finding the best neural network configuration for a given goal can be challenging, especially when it is not possible to assess the output quality of a network automatically.  ...  synthesis with recurrent neural networks.  ...  In the remainder of this work, we discuss related approaches concerning the interactive text synthesis without and with neural networks.  ... 
dblp:conf/esann/SchlegelCBK18 fatcat:govkkba4dbffzbwzuiqkq6yffm

Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination [chapter]

Mohsin Abbas, Peter van Rosmalen, Marco Kalz
2019 Lecture Notes in Computer Science  
Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically.  ...  Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted.  ...  Machine learning algorithms such as Neural Networks can be used to create models using a corpus of scored texts.  ... 
doi:10.1007/978-3-030-29736-7_30 fatcat:th3m62e72bb2bmw4ay4rdlfciy

A survey automatic text summarization

Oguzhan Tas, Farzad Kiyani
2017 Pressacademia  
An abstractive summarization is used to understanding the main concepts in a given document and then expresses those concepts in clear natural language.  ...  Text summarization is compress the source text into a diminished version conserving its information content and overall meaning.  ...  Another technique [18] which is using Neural Network for text summarizing add "Numerical Data Feture" to feature input list so their network uses 8 input neurons.  ... 
doi:10.17261/pressacademia.2017.591 fatcat:j5zd56uytfhapfmh3cl2fi4dqm

An Efficient Technique for Image Captioning using Deep Neural Network [article]

Borneel Bikash Phukan, Amiya Ranjan Panda
2020 arXiv   pre-print
Every entity in internet must be properly identified and managed and therefore in the case of image data, automatic captioning for identification is required.  ...  This paper discusses an efficient and unique way to perform automatic image captioning on individual image and discusses strategies to improve its performances and functionalities.  ...  Hence, the goal of this paper is to describe an efficient method to automatically generate caption for an image using deep neural network approach.  ... 
arXiv:2009.02565v1 fatcat:q6vfq6vctrgjroh2xpy2ochlzq

Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization

D.Y. Sakhare, Raj Kumar
2014 International Journal of Information Technology and Computer Science  
Here, two neural networks are trained based on the feature score and the syntactic structure of sentences.  ...  Finally, the two neural networks are combined with weighted average to find the sentence score of the sentences.  ...  Here, weighted average formula (6) is used to combine the sentence score of both neural networks.  ... 
doi:10.5815/ijitcs.2014.03.05 fatcat:ezxmy7nvfnhvhohaabxzdz5mgq

Speech Recognition and Speech Synthesis Models for Micro Devices

Bismark Asiedu Asante, Hiroki Imamura, N. Moubayed
2019 ITM Web of Conferences  
In this article, we developed a smaller Deep Neural Network models for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) for communication on micro devices such as Raspberry Pi.  ...  With the advent and breakthrough of interaction between humans and electronic devices using speech in communication, we have seen a lot of applications using speech recognition and speech synthesis technology  ...  Model Architecture In this paper, we introduce two deep neural networks for an Automatic Speech Recognition (ASR) and Text-to-speech (TTS) system using speech synthesis in micro devices.  ... 
doi:10.1051/itmconf/20192705001 fatcat:sy5e3bi455ezndip5xhld5tswm
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