Natural Language Processing

Natural Language Processingnatural language processing, machine learning sample, customer churn, natural language understanding, , sentiment analysis, semantic analytics, text analysis, text classification, document clustering, cluster analysis, key phrase extraction, language detection, named entity recognition, syntax analysis, keywords, intent, categorization, sentiments, entities, metadata, emotions, topics, artificial intelligence, predictive modeling, prescriptive modeling, data reimagined,  quantitative analysis, qualitative analysis

We are natural language processing (NLP) practitioners, skilled at helping you use this technology as a business intelligence tool.  Most importantly, we believe strongly in practical AI.

Natural Language Processing Services

As with all of our services, we try to provide information derived from natural language processing that you can understand and that meets your expectations.  It is $with value NLP information.  Our services are not limited to machine-generated results, as we also have data extraction and data analysis capabilities that integrate best practices qualitative and quantitative assessment.

  • Key Phrase Extraction
  • Language ID
  • File Classification
  • Named Entity Recognition
  • Sentiment Analysis
  • Text Classification
  • Custom Classification

Custom Projects

The range of custom work that we do in this area spans all natural language processing applications.  Below are a few examples of the models we can create for you.  

  • Classification
  • Named Entity Recognition 
  • Document Clustering  
  • Topic Modeling

 

Healthcare and Natural Language Processing

We have deep experience in the healthcare field and, in particular, in creating healthcare natural language processing models.  Most health record data is unstructured data--clinical text, physician's notes, discharge summaries, test results, case notes, and so on. The models we create enable us to sort through this data and retrieve the information that you need.  Some of the uses for these models would be:

  • Patient Case Management and Outcome
  • Clinical Research
  • Medical Billing and Healthcare Revenue Cycle Management

NLP and The Enigma Code

Alan Turing, Turing Test, natural language processing, machine learning sample, customer churn, natural language understanding, , sentiment analysis, semantic analytics, text analysis, text classification, document clustering, cluster analysis, key phrase extraction, language detection, named entity recognition, syntax analysis, keywords, intent, categorization, sentiments, entities, metadata, emotions, topics, artificial intelligence, predictive modeling, prescriptive modeling, data reimagined,  quantitative analysis, qualitative analysisThe history of Natural Language Processing can be traced back to the 1950s, first proposed by Alan Turing (who cracked the Enigma Code) in the form of a simple test, the Turing Test, to determine if a machine can be considered intelligent.

A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.
                                          -Alan Turing

Turing proposed that evaluators have conversations with both humans and machines to test whether they could consistently tell apart the two agents. The conversation would be limited to a text-only channel to create a level playing field. During the test, if the evaluator fails to continuously distinguish machine from human, the machine is said to have passed the Turing test.

One could say that a man can 'inject' an idea into the machine, and that it will respond to a certain extent and then drop into quiescence, like a piano string struck by a hammer
                                         - Alan Turing

The Meaning of What Was Said and What Was Not Said

natural language processing, machine learning sample, customer churn, natural language understanding, , sentiment analysis, semantic analytics, text analysis, text classification, document clustering, cluster analysis, key phrase extraction, language detection, named entity recognition, syntax analysis, knowledge base, knowledge management, keywords, intent, categorization, sentiments, entities, metadata, emotions, topics, artificial intelligence, predictive modeling, prescriptive modeling, data reimagined, predictive modeling, quantitative analysis, qualitative analysis, survey, max diff, conjoint, survey, max diff, conjoint, employee satisfaction, customer satisfaction, brand awareness, discrete choiceA comprehensive text analysis is rich in information about what was said and what was not said.  Used often to gauge customer sentiment, tools like this can offer actionable insights into customer behavior.

  • Entities – Who, what, when and where.  What is it and how much is it?  What is happening and when?   Our analysis detects and records the entities in your text by category and confidence level.
  • Key phrases – Key phrases, those of relative importance, are detected and scored based on the confidence level that they are in fact key phrases.  Key phrases are typically one to five words long that appear verbatim in a document, and can be used to briefly summarize its content. For example, a document about the GDPR might return key phrases such as General Data Protection Regulation, the current directive and data security principles.
  • Language – This analysis detects and records the dominant language of the text.
  • Sentiment – Sentiment analysis is contextual mining of text which identifies and extracts subjective information—i.e., it can identify and categorize opinion.  This analysis detects whether sentiment is positive, neutral, negative, or mixed; and, a confidence level is recorded for each. Customer thumbs-up or thumbs-down on a programming change that will require them to relearn parts of an existing software would be a sample of sentiment analysis.
  • Syntax – This part of the analysis parses each word in your document and records its part of speech with the level of confidence for that determination.