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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>

Retrieval according to Events and Times. Our long term intension is to build a system which automatically extracts Events and Time expressions and ordering them in a particular order. Ordering of events become significant task and it is assists to finding all feasible times a given event can occur, all relationships between two given events, finding one or more consistent scenarios and finally representing data in a minimal network form.In this paper, we are focusing about automatic extraction of Quantitative, Qualitative time's information and from Legal Text Documents, along with this Legal text expressed in natural language can be automatically annotated with semantic mark ups using natural language processing Techniques. Finally applied reasoning among temporal information with the help of extracted information. Reasoning can be done using constraint satisfaction networks by applying Allen's Algebra relations. Apart from this result analysis obtained using Precision and Recall statistical measurements over standard dataset DUC 2005.

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