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Natural Language Processing

Natural Language Processing. Guangyan Song. What is NLP. Natural Language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages. Goal Natural Language Understanding Natural Language Generation.

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Natural Language Processing

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  1. Natural Language Processing Guangyan Song

  2. What is NLP • Natural Language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages. • Goal • Natural Language Understanding • Natural Language Generation

  3. Example Applications • Automatic summarization • Machine Translation • Information Retrieval • Question Answering system • Foreign language written aid

  4. Problems • Natural Languages are very complex • Many words have various meaning • The number of relevant dependencies is much too large and those dependencies are too complex

  5. Major Approaches • Rule based NLP • Handcrafted linguistic rules • Very labour-intensive and difficult to scale up • Example based NLP • Search for similar examples from training data • Statistical based NLP • Learn from training data and generate natural language

  6. Machine Translation • Microsoft Bing Translator • Early used Rule based technology • Morphology • Lexical • Syntactic

  7. Machine Translation • Now using Statistical based approach

  8. Information Retrieval • Stop-Words Removal • Stemming

  9. Information Retrieval • Language Model Retrieval • Similar as Statistical based Machine translation approach • NLP technologies are not widely used in web search

  10. Foreign Language Writing aid • Microsoft Grammar checker • English Second Language (ESL) Assistant • Example based approach

  11. Information extraction • Email2DB • Get stock information from emails and stored in the database • AddressDoctor • Analyze unstructured or partly structured addresses and divide them into individual elements • Recognize countries (by Name, ISO codes, major cities, etc.) • Format addresses according to the postal rules of all licensed countries • Standardize address elements (i.e. avenue -> ave, street -> st or vice versa) • Mainly rule based approach

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