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AI - Weeks 19 & 20 Natural Language Processing

Lee McCluskey, room 2/07 Email lee@hud.ac.uk http://scom.hud.ac.uk/scomtlm/cha2555/. AI - Weeks 19 & 20 Natural Language Processing. History: The Turing Test.

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AI - Weeks 19 & 20 Natural Language Processing

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  1. Lee McCluskey, room 2/07 Email lee@hud.ac.uk http://scom.hud.ac.uk/scomtlm/cha2555/ AI - Weeks 19 & 20Natural Language Processing Natural Language Processing

  2. Natural Language Processing History: The Turing Test Assume person A communicates by “text/email” to 1) a person and 2) a machine. The Turing Test is for A to determine which is the computer and which is the person by the text responses. Assume A continues to ask 1) and 2) questions by written text and elicits responses. If, from the responses over time, the person cannot tell the difference between 1) and 2), then the Turing Test is passed. Up to now, no system has got close to passing the Turing Test. It is possible to apply the “Turing Test” metaphorically to other areas of computing eg the Turing Test for the game of chess could be said to be passed.

  3. Natural Language Processing NLP – the problem Text (sentence, email, news story..) UNDERSTANDING PROCESS (“Natural Language Understanding”) Knowledge Base: representation of meaning Translation Natural Language Generation Summary/ Classification

  4. Natural Language Processing NLP is NOT SPEECH RECOGNITION Speech Understanding is FAR HARDER than NLP but is potentially much more valuable because: Translating Speech → Text we loose MANY visual and aural clues Eg Tone, speed, emotion within voice, accent Facial expressions, arm movements, “body language” All contribute to the meaning of the utterance as well as the plain text In text there are no sound cues, or visual cues, available to give extra meaning to the text. So we might raise our voice to show our anger, or make gestures to add to the description of a shape. Without these extra cues, it is much harder to do NLP.

  5. Natural Language Processing Some Potential/Current Applications of NLP -- Q & A services eg automated quiz answering services. These need to understand the question enough so that they can choose the correct answer from eg an online search --chatbots – online programs that get into conversation with you for entertainment eg Eliza --natural language translators – (online) services that take text in one language and translate it to another language eg English -> German -- natural language generation – eg games that need to communicate to the user in text or generate news stories / running commentaries as part of the game -- text summarisation or categorization (news stories, spam filters, document classifiers …)

  6. Natural Language Processing NLP: the process Text (sentence, email, news story..) -- Parsing -- Referencing -- Meaning Extraction and Integration UNDERSTANDING PROCESS (“Natural Language Understanding”) Knowledge Base: representation of meaning Translation Natural Language Generation Summary/ Classification

  7. Natural Language Processing NLP: the process Parsing - breaking down the sentence into components (words), checking that these conform to a grammar (are syntactically correct) and possibly outputting a parses tree Referencing – finding the “actual” references to words in a sentence, and resolving ambiguities Eg “Old friend” does “old” describe friend, or does it describe the referent of friend? Meaning Extraction and Integration – translating the parse, and noun references, into an internal representation language (eg First order logic) and integrating it with other “knowledge” eg “All men are mortal” – recognising that “men” refers to a species held in a taxonomic knowledge base about living things in the KB.

  8. Natural Language Processing Parsing – a small grammar for English sentence --> noun_phrase, verb_phrase. noun_phrase --> determiner, adjective, noun. noun_phrase --> adjective, noun. noun_phrase --> determiner, noun. noun_phrase --> noun. verb_phrase --> verb, noun_phrase. verb_phrase --> verb, preposition, noun_phrase. determiner --> a | an. adjective --> fruit. noun --> flies | fruit | time | arrow. noun --> banana. verb --> like | flies. preposition --> like

  9. Natural Language Processing Summary - Challenges -Biggest challenge is adequacy of interpretation: translating the prose into some “representation of its meaning” adequate for the purposes of the application eg QA, translation -The above is exacerbated by ambiguity problems – mapping every sentence into just one representation - mapping individual words into one meaning -There is no standard grammar for NLs -NL changes over time, words go in and out of currency-NL contains lots of proper names – causing more problems with identity and determining referents for nouns.

  10. Natural Language Processing Practical / Tutorial | -- Work on the Exercises on Logic Reasoning from last week -- Get help on the coursework if required Next Week we will look at how PROLOG can be used to implement NLP

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