Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Reasoning with temporal information in natural language text has attracted great attention due to its potential applications in summarization, question answering and other tasks. For example, the chronological ordering of events described in a text is important for presenting the information in the summary. Linking information in a natural language text with temporal relations is essential in question answering system to address time sensitive and dynamic world. A crucial first step towards the computational treatment of the temporal information in these applications is the automatic extraction of events described in the text and identification of temporal relations to link these events. Much of the work done in this direction can be classified as -Annotation schemes for identification of events and time implicit in the text, linking the events using temporal relations and Temporal reasoning for solving practical applications.The present paper is a survey of various proposals to address these issues. Various annotation schemas developed to represent temporal information in natural language text. A discussion of the frameworks for temporal reasoning and tractable classes is described. The usefulness of these models to applications such as summarization and question answering are also presented.

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<p>Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure.  As a result, a graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, duringetc) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the proposed framework with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.</p>

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