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ISP 433/633 Week 9

ISP 433/633 Week 9. NLP in IR. Natural Language Processing. Simple Definition: A study of how to use computers to do things with human languages. What to process? Many levels. Levels of NLP. Phonetics and Phonology How do sounds map to words?; Speech Recognition Morphology

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ISP 433/633 Week 9

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  1. ISP 433/633 Week 9 NLP in IR

  2. Natural Language Processing • Simple Definition: • A study of how to use computers to do things with human languages. • What to process? • Many levels

  3. Levels of NLP • Phonetics and Phonology • How do sounds map to words?; Speech Recognition • Morphology • How do parts of words make up words?; Lexical analysis: friend+ly • Syntax • How do words and phrases combine to make bigger structures (sentences)?; Grammars, Parsing

  4. Levels of NLP (II) • Semantics • What do words and their combination mean?; Semantic analysis • Pragmatics • What goes on beyond semantics?; Sentence meaning + context, discourse analysis. Can you open the window?

  5. Ambiguity in Natural Language • Lexical ambiguity • For example, the English word ‘bank’ is ambiguous between ‘side of the river’ and ‘financial institution’ • Syntactic ambiguity: the ambiguity in sentence structure • “John saw a woman with a telescope. • Semantic ambiguity • Visiting relatives can be boring.

  6. Use of NLP in IR • Phrase usage vs. individual terms • Search expansion using related terms/concepts • Language reuse • Information Extraction • Question Answering • User Interface • Speech recognition • Text-to-speech

  7. Phrase for indexing • single terms often inadequate for accurate discrimination • phrases offer alternative • for huge DB’s use of phrasal terms becomes necessary • E.g.: “joint”, “venture” may get small weighting separately

  8. How to select a phrase • co-occurrence selection leads to high error rates • E.g. The former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin • syntactic analysis needed TAGGER PARSER TERMS

  9. Tagging INPUT SENTENCE The former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin. TAGGED SENTENCE The/dt former/jj Soviet/jj President/nn has/vbz been/vbn a/dt local/jj hero/nn ever/rb since/in a/dt Russian/jj tank/nn invaded/vbd Wisconsin/np ./per

  10. Parsing [assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] [sub_ord[SINCE [[verb[INVADE]] [subject [np [n TANK][t_pos A] [adj [RUSSIAN]]]] [object [np [name [WISCONSIN]]]]]]]]]

  11. Term Weighting President 2.623519 soviet 5.416102 President+soviet 11.556747 president+former 14.594883 Hero 7.896426 hero+local 14.314775 Invade 8.435012 tank 6.848128 Tank+invade 17.402237 tank+russian 16.030809 Russian 7.383342 wisconsin 7.785689

  12. Language Reuse NP Yugoslav President Slobodan Milosevic [description] [entity] Phrase to be reused

  13. Named entities Richard Butler met Tareq Aziz Monday after rejecting Iraqi attempts to set deadlines for finishing his work. Yitzhak Mordechai will meet Mahmoud Abbas at 7 p.m. (1600 GMT) in Tel Aviv after a 16-month-long impasse in peacemaking. Sinn Fein deferred a vote on Northern Ireland's peace deal Sunday. Hundreds of troops patrolled Dili on Friday during the anniversary of Indonesia's 1976 annexation of the territory.

  14. Entities + Descriptions Chief U.N. arms inspector Richard Butler met Iraq’s Deputy Prime MinisterTareq Aziz Monday after rejecting Iraqi attempts to set deadlines for finishing his work. Israel's Defense MinisterYitzhak Mordechai will meet senior Palestinian negotiatorMahmoud Abbas at 7 p.m. (1600 GMT) in Tel Aviv after a 16-month-long impasse in peacemaking. Sinn Fein, the political wing of the Irish Republican Army, deferred a vote on Northern Ireland's peace deal Sunday. Hundreds of troops patrolled Dili, the Timorese capital, on Friday during the anniversary of Indonesia's 1976 annexation of the territory.

  15. Multiple descriptions per entity Bill Clinton U.S. President President An Arkansas native Democratic presidential candidate Profile for Bill Clinton

  16. Information Extraction (IE) • To extract information that fits pre-defined database schemas or templates • Given a text, answer: • What happened? • When? • Where? • What was the outcome? • Who? • …

  17. Bridgestone Sports Co. said Friday it had set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be supplied to Japan. The joint venture, Bridgestone Sports Taiwan Co., capitalized at 20 million new Taiwan dollars, will start production in January 1990 with production of 20,000 iron and “metal wood” clubs a month. ACTIVITY-1 Activity: PRODUCTION Company: “Bridgestone Sports Taiwan Co.” Product: “iron and ‘metal wood’ clubs” Start Date: DURING: January 1990 TIE-UP-1 Relationship: TIE-UP Entities: “Bridgestone Sport Co.” “a local concern” “a Japanese trading house” Joint Venture Company: “Bridgestone Sports Taiwan Co.” Activity: ACTIVITY-1 Amount: NT$200000000 IE example

  18. Another text ………. Jurgen Pfrang, 51, reportedly stumbled upon the robbers on the second floor of his Nanjing home early on Sunday. The deputy general manager of Yaxing Benz, a Sino-German joint venture that makes buses and bus chassis in nearby Yangzhou, was hacked to death with 45 cm watermelon knives. ………. Name of the Venture: Yaxing Benz Products: buses and bus chassis Location: Yangzhou,China Companies involved: (1)Name: X? Country: German (2)Name: Y? Country: China

  19. Need another template ………. Jurgen Pfrang, 51, reportedly stumbled upon the robbers on the second floor of his Nanjing home early on Sunday. The deputy general manager of Yaxing Benz, a Sino-German joint venture that makes buses and bus chassis in nearby Yangzhou, was hacked to death with 45 cm watermelon knives. ………. Crime-Type: Murder Type: Stabbing The killed: Name: Jurgen Pfrang Age: 51 Profession: Deputy general manager Location: Nanjing, China

  20. Limitations of IE • Templates are hand-crafted by human experts • Templates are domain dependent and not easily portable • Templates = pre-defined questions

  21. Question answering Q: When did Nelson Mandela become president of South Africa? A: 10 May 1994 Q: How tall is the Matterhorn? A: The institute revised the Matterhorn 's height to 14,776 feet 9 inches Q: How tall is the replica of the Matterhorn at Disneyland? A: In fact he has climbed the 147-foot Matterhorn at Disneyland every week end for the last 3 1/2 years • TREC Question answering track • Eight years • Corpus: texts and questions

  22. Example systems • AskJeeves is probably most well known example • AnswerBus is an open-domain question answering system • Ionaut, EasyAsk, AnswerLogic, AnswerFriend, Start, LCC, Quasm, Mulder, Webclopedia, etc.

  23. Type of Answer

  24. Steps • Usually IR methods are used to retrieve candidate documents • NLP techniques are used to extract the likely answers from the text of the documents • Question type • Name entities extraction • Co-reference resolution

  25. Type of Question

  26. AnswerBus

  27. Automatic Speech Recognition • Hand-free input of query • Usually need to train the ASR engine • User speaks known text to engine • Type • Grammar based • Controlled vocabulary, defined sentence structure, only for small domains • More accurate • Free dictation • Less accurate

  28. Text To Speech • Usually for vision-impaired users • Demo: http://www.research.att.com/projects/tts/demo.html

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