![]() If I handled two events and need to generate a narrative for both of them, I would go for multiple sections. We can create multiple sections and multiple paragraphs within a section. I will join both above sentences and create a paragraph to get a complete narrative. sub2 = nlgFactory.createNounPhrase('number of developers') tDeterminer("the")Īnd, our next generated text: The number of developers attended is 30.0. Similarly, I can generate a second sentence. if(project_event_type !='Webinar'): at_preposition = nlgFactory.createPrepositionPhrase("at")īelow is the generated text using NLG: Rajesh Gudikoti conducted "Natural Language Processing" Meetup on T00:00:00.000000000 at Bangalore. For other event types, like meetups or conferences, I need to mention the location where the event was held. If the event type is a webinar type of event, I do not need the location of event. ![]() clause_1 = nlgFactory.createClause() sub1 = nlgFactory.createNounPhrase(speakers) verb1 = nlgFactory.createVerbPhrase("conduct") #IBM DATA GENERATOR CODE#I have provided some pseudo code for reference. Now, it is time to use NLG (Natural Language Generation)!! Instead of typing a narrative out, I would like to generate a narrative. Natural Language Generation is a technique to produce narrative out of structured dataset. This is where Natural Language Generation(NLG) will help us. Problem Statement : How can we go about generating an event narrative by automatically picking the data from database? The details(data) mentioned in quotes are picked from the tool or database. “Rajesh Gudikoti” handled session on “Natural Language Processing”. We conducted a meetup event on “March 20, 2019” in “Bangalore”. Normally, when such a request comes, I provide a description as below. When a publishing team asks to provide a narrative of the event which we handled(completed), we can refer to the tool and create a brief text from the data within. ![]() There are other fields like expected number of people for the event, etc., but for simplicity, I would refer only above 6 fields. The general practice when we create a newsletter is to employ a tool with individual entries that include: In this post, I will be showcasing the power of natural language generation, or NLG, with the business case of generating a newsletter. Generate newsletter automatically using NLG: Part 1 ![]()
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