![]() Next we add a Dropout layer with value 0.5. The second layer accepts 300 features as input and 64 features as output. ![]() The first layer accepts all features from the Embedding Layer (Word2Vec) as input and passes 300 features as output to the second LSTM layer. Features from this model are passed through our LSTM layers. The Word2Vec model acts as an Embedding Layer in a neural network. Features are generated by passing the essays through Word2Vec model. This model makes sense of the available words by assigning numerical vector values to each word. This list is fed into the Word2Vec model. We create a list of words from each sentence and from each essay. You can find in the below link or download from the Dataset folder. ![]() The dataset we are using is ‘The Hewlett Foundation: Automated Essay Scoring Dataset’ by ASAP. The project aims to develop an automated essay assessment system by use of machine learning techniques and Neural networks by classifying a corpus of textual entities into a small number of discrete categories, corresponding to possible grades. Why AES?Īutomated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The process of automating the assessment process could be useful for both educators and learners since it encourages the iterative improvements of students' writings. It can be defined as the process of scoring written essays using computer programs. Automated Essay Scoring (AES) is a tool for evaluating and scoring of essays written in response to specific prompts.
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