Building Graph for Events and Time in Natural Language Text
Vanitha Guda1, Suresh Kumar Sanampudi2
1Vanitha Guda*, Department of CSE,Chaithanya Bharathi Institute of Technology, Hyderabad, India.
2Suresh Kumar Sanampudi, Department of IT JNTUCEJ, Nachupally Jagityal Karimnagar, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 581-586 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8419019320/2020©BEIESP | DOI: 10.35940/ijitee.C8419.019320
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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: Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work.
Keywords: Events, Time, Event-Time Graph, Question Answering systems.
Scope of the Article: Natural Language Processing