Natural Language Processing Neelnavo Kar

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Natural Language Processing

  • Neelnavo Kar

  • Alex Huntress-Reeve

  • Robert Huang

  • Dennis Li

What is Natural Language Processing?

    • NLP is an interdisciplinary field that uses computational methods to:
      • Investigate the properties of written human language and model the cognitive mechanisms underlying the understanding and production of written language.
      • Develop novel practical applications involving the intelligent processing of written human language by computer.

What is NLP? (cont.)

    • NLP plays a big part in Machine learning techniques:
      • automating the construction and adaptation of machine dictionaries
      • modeling human agents' desires and beliefs
        • essential component of NLP
        • closer to AI
    • We will focus on two main types of NLP:
      • Human-Computer Dialogue Systems
      • Machine Translation

Human-Computer Dialogue Systems

    • Usually with the computer modelling a human dialogue participant
    • Will be able: 
      • To converse in similar linguistic style
      • Discuss the topic
      • Hopefully teach

Current Capabilities of Dialogue Systems

    • Simple voice communication with machines
      • Personal computers
      • Interactive answering machines
      • Voice dialing of mobile telephones
      • Vehicle systems
      • Can access online as well as stored information
    • Currently working to improve

The Future of H-C Dialogue Systems

    • The final end result of human computer dialogue systems:
      • Seamless spoken interaction between a computer and a human
    • This would be a major component of making an AI that can pass the Turing Test
    • Be able to have a computer function as a teacher

Human Computer Dialogue in Fiction

    • Halo's Cortana AI
      • Made from models of a real human brain
      • Made to run the ship
      • Made very human conversations
    • Ender's Game series: Jane
      • Made from "philotic connection"
      • Human conversation

Problems of Human-Computer Dialogue

    • At the moment, most common computer dialogue systems (call systems, chatter bots, etc.) cannot handle arbitrary input
      • In many cases, the computer can only respond to "expected" speech
      • Call systems often compensate with "Sorry, I didn't get that," when something unexpected is said.

Problems of Human-Computer Dialogue

    • Computers need to be able to learn and process colloquial speech
    • Needed to understand informal speakers:
      • Understanding varied responses for call systems
      • Accounting for variations in spoken numbers
    • Processing colloquialisms is also necessary for seamless dialogue, where the computer must avoid sounding too formal
      • John Connor: "No, no, no, no. You gotta listen to the way people talk. You don't say 'affirmative,' or [stuff] like that. You say 'no problemo.' "

Successes of Human-Computer Dialogue

    • So far, human-computer dialogue has been most successful in applications where information about a specific topic is sought from the computer.
      • Electronic calling systems: company-specific
      • Travel agents: specific to an airline or destination
    • However, more complex systems of human-computer dialogue have been produced which can interpret more varied input.
      • Physics tutoring system (ITSPOKE) which can analyze and explain errors in the response to a physics problem.
      • Allows for more complex input than "Yes," "No," or "Flight UA-93"
    • These still cannot compare to true human-human dialogue.

Machine Translation

    • Important for:
      • accessing information in a foreign language
      • communication with speakers of other languages
    • The majority of documents on the world wide web are in languages other than English

Statistical Translation

    • Rule based
    • Works relatively well with large sets of data
    • Used probability to translate text
    • Natural translations
    • Google

Example Based Translation

    • Converts "parallel" lines of text between language
    • Only accurate for simple lines
    • Minimal pairs are easy
    • Analogy based


    • Takes words and makes them simpler automatically
    • For example in Spanish conjugated words like usado may be changed to usar

Future of Machine Translation

    • Goal:
      • Aim to be able to flawlessly translate languages
    • Link Human-Computer Dialogue and Machine Translation
    • Have someone be able to talk in one language to a computer, translate for another person
    • Translated Video Chat

Machine Translation in Fiction

    • Star Wars: C-3P0
      • Interpreter
      • Could hear and translate alien languages
      • Final goal of machine translation
    • Star Trek: Universal Translator
      • Computer can seamlessly translate alien languages


    • Works well only with predictable texts.
    • Doesn't work well with domains where people want translation the most: 
      • spontaneous conversations
      • in person
      • on the telephone
      • and on the Internet.


    • Computers can't deal with ambiguity, syntactic irregularity, multiple word meanings and the influence of context.
  • Time flies like an arrow.

  • Fruit flies like a banana.

    • Accurate translation requires an understanding of the text, situation, and a lot of facts about the world in general.
  • The box is in the pen. 


    • The sign is describing a restaurant (the Chinese text, 餐厅, means "dining hall"). 
    • In the process of making the sign, the producers tried to translate Chinese text into English with a machine translation system, but the software didn't work, producing the error message, 


    • Product knowledge bases need to be translated into multiple languages
    • Hiring a large multilingual support staff is expensive
    • Machine translation is cheaper and accurate with predictable texts.
    • Microsoft, Autodesk, Symantec, and Intel use it.
      • Makes customers happy
      • Still readable though slightly chunkier than human translations

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