Natural language processing

Natural language processing describes the technology that allows computers to understand and process human speech and text, deciphering human language using a knowledge base that must be constantly updated. NLP is often associated with artificial intelligence (AI) and machine learning, allowing computers to examine the data and statistics in order to find patterns that otherwise would go unnoticed. The more data it analyzes, the more accurate a program becomes while increasing its understanding of human language and speech.


  • Enterprises use NLP technology for streamlining their documentation processes
  • NLP can be used for better on-site search, as it provides more accurate search results while mining through more data by deciphering exactly what the user wants to look for on a website
  • In the healthcare industry, NLP helps doctors better understand patients with mental illnesses. Additionally, NLP can be used for analyzing health records and finding correlations between symptoms and diagnostics
  • NLP can automatically translate full sentences from one language to another and it can do so even if a sentence has typos while understanding the context


  • When it comes to spoken language, NLP systems still have trouble understanding accents
  • NLP is an expensive technology, which requires a lot of computing power
  • NLP programs have low accuracy upon implementation and they still have to undergo various types of training before providing error-free results


  • Grammatical rules
  • Definitions and dictionary
  • Part-of-speech
  • Entity extraction
  • Syntactic analysis
  • Semantic analysis
  • Sentiment analysis
  • Pragmatic analysis
  • Output processing

Development tools

  • Apache OpenNLP
  • Solr
  • Natural Language Toolkit (NLTK)
  • Stanford Core NLP Suite
  • Apache Lucene
  • GATE
  • Apache UIMA
  • MaltParser
  • Kyfd
  • Travatar
  • Chasen
  • CMU Sphinx
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