Natural Language Processing Neelnavo Kar

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

Paraphrasing

• 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

Problems

• 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.

Problems

• 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. 

Problems

• 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, 

Successes

• 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