Event -Time Relation in Natural Language Text
Vanitha Guda1, Suresh Kumar Sanampudi2

1Vanitha Guda*, Department of CSE, Chaithanya Bharathi Institute of Technology (A) Gandipet, Hyderabad , Telangana, India.
2Dr S.Suresh Kumar, IT Department, JNTUCEJ, Nachupally Jagityal Karimnagar, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 716-724 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7976088619/2019©BEIESP | DOI: 10.35940/ijeat.F7976.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing(NLP), “Events” are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP applications 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, during, etc.) 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 Ev T Extract 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 work with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.
Keywords: Natural Language Processing, Events, Times, Event-Time Graph, Temporal Question Answering.