Manual grading of essays by humans is time-consuming and likely to be susceptible to inconsistencies and inaccuracies. In recent years, an abundance of research has been done to automate essay evaluation processes, yet little has been done to take into consideration the syntax, semantic coherence and sentiments of the essay's text together. Our proposed system incorporates not just the rule-based grammar and surface level coherence check but also includes the semantic similarity of the sentences. We propose to use Graph-based relationships within the essay's content and polarity of opinion expressions. Semantic similarity is determined between each statement of the essay to form these Graph-based spatial relationships and novel features are obtained from it. Our algorithm uses 23 salient features with high predictive power, which is less than the current systems while considering every aspect to cover the dimensions that a human grader focuses on. Fewer features help us get rid of the redundancies of the data so that the predictions are based on more representative features and are robust to noisy data. The prediction of the scores is done with neural networks using the data released by the ASAP competition held by Kaggle. The resulting agreement between human grader's score and the system's prediction is measured using Quadratic Weighted Kappa (QWK). Our system produces a QWK of 0.793. INDEX TERMS Natural language processing, semantic analysis, sentiment analysis and text mining, automated essay evaluation.

The semantic analysis field has a crucial role to play in the research related to text analytics. Calculating the semantic similarity between sentences is a long-standing problem in the area of natural language processing, and it differs significantly as the domain of operation differs. In this paper, we present a methodology that can be applied across multiple domains by incorporating corpora-based statistics into a standardized semantic similarity algorithm. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. When tested on both benchmark standards and mean human similarity dataset, the methodology achieves a high correlation value for both word (r = 0.8753) and sentence similarity (r = 0.8793) concerning Rubenstein and Goodenough standard and the SICK dataset (r = 0.8324 1) outperforming other unsupervised models.

Within the field of post-secondary student mobility, the assessment, and evaluation of transfer credit is a labor-intensive human intelligence task that is subject to time limits and human bias. This paper introduces a semi-automated approach to assessing transfer credit and generating articulation agreements between post-secondary institutions using natural language processing (NLP). The output from the NLP system is tested using a content expert generated an assessment of transfer credit between computer science programs at two separate post-secondary institutions. Initial testing with an unsupervised NLP algorithm, despite good results against standardized measures, assessed the percentage of course overlap as 71% similar to the percentages selected by human content experts. The application of an algorithm based on the Word2Vec model using domain-specific Wikipedia corpus and dependency parsing was applied to compensate for domain specific language and improved the relationship between content experts ratings and NLP output to 86% related overlap. INDEX TERMSArticulation agreement, natural language processing, semantic similarity, student mobility, transfer credit, word embeddings.