Artificial intelligence and machine learning , have come a long way in the past decade.
What began as an idea to generate human-sounding narratives and reports, such as for weather, finance, and sports, now we are at a place in time where NLG can interact with humans on the more subjective topics of communications. Computational linguistics engineers and machine learning experts can now leverage massive data sets to train machines to take on a variety of personas, artificial beings capable of holding long conversations with real people, undetected by the untrained eye.
In the coming decade, NLG will be utilized to engage students in the virtual classroom, surgeons at the operating table, even criminal operatives on social media.
All communications have some degree of polarity. Other than when involving close friends and family, the sender of a message can not precisely predict the receiver’s innermost feelings. As writers, we can only rely on learning from the reader’s disclosed response to the message.

Advanced Natural Language Processing Tools
Today’s natural language processing applications are capable of processing every word ever published on the internet, including vocal recordings, video voiceover, and image-imbedded words. Most of these words, with the exception of named entity words, are represented in frequencies of millions or billions across the web. Natural language processing technologies enables us to drill down into these words, sifting through language by subject, topic, and context. The words in a given language comprise a relatively small set of phrases and grammar rules. In fact, every word ever written bears some measurable degree of relational context to every other word ever written. This can be viewed as a type of statistical mapping of languages. The word, “cat” has a measurable relationship with the word, “beach”. While there aren’t many written references of cats on beaches, there is still some measurable degree of correlation between these two very different words. The spacial relationship between words can often, but not always, narrow when you introduce a new word in the contextual space, for example, adding the word “hotel” to the word map, could retrieve context frames such as, “Just took our cat to the pet hotel. We’ll see you on the beach tomorrow.” As a human, the neural network in your brain can process hundreds of context relationships between the words “cat”, “hotel” and “beach”. Natural language processing can work in a similar manner, using recurrent neural networks and probabilistic language modeling to classify exactly how all types of language is used, with frequency of usage as the primary source of validation. Validating the word, “the”, before the word, “go”, for example would score very low on usage frequency, but would return classifiable metrics in context with some slang usage, as well as named entities, for example, the early 80’s pop group, “The Go-Go’s”

language as a phenomenon
artificial inteligence
non-Enlgish NLP
deep learning
word embeddings
NLP toolkits
noisy data
unstructured data
textual data
entity extraction
document classification
topic modeling
natural language understanding
training data
computational linguistics
related fileds
NLP methods
information extraction
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knowledge databases
language ontologies
text pre-processing
text normalization
POS tagging
sentiment analysis
source code management
research methods
communicating insights
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diverse audience
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computer science
numeric discipline
text analytics
text classification
topic detection
named entity recognition
entity resolution
dialog systems
event detection
language modeling
hands-on experience
hyper-parameter tuning
deep construction
deep distribution
CNN convolutional neural network
RNN recurrent neural network
LSTM long-short term memory units of a neural network
large scale text mining
multi-domain text corpora
text streams
data streams
junior data scientist
real-time computing
integrated deep learning
mobile apps
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product development team
knowledge extraction
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semantic role labeling
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compelling recommendations
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complex data analysis
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machine learning algorithms
AI techniques
productizing of intelligence
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detecting outliers
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reusable components
peer review
NLP applications
NLP modeling
evaluating training data
utilizing training data
word embedding
sentiment analysis
confidence intervals
relevant statistical measures
key paramaters
affect their performance
machine learning techniques
large amounts of text data
extracting insights
N-gram modeling
document classification
text pre-processing
POS tatting
conversational AI
dialig management
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new metrics
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brand’s audience
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connect better with their audiences
computational methods
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provide more value to their audiences
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use of data in
increases its focus
focus on its audience
what’s known about the audience
data scientist
shaping the key metrics
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content reach
basic copywriting workflow
content types
what has worked well
data dashboards
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template writing
applying computational methods