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– 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

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?

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.

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

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

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

Tagging

INPUT SENTENCEThe former Soviet President has been a local hero ever sincea Russian tank invaded Wisconsin.

TAGGED SENTENCEThe/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

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]]]]]]]]]

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

Language Reuse

Yugoslav President Slobodan Milosevic

[description]

NP

Phrase to be reused

[entity]

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.

Entities + Descriptions

Chief U.N. arms inspector Richard Butler met Iraq’s Deputy Prime Minister Tareq Aziz Monday after rejecting Iraqi attempts to set deadlines for finishing his work.

Israel's Defense Minister Yitzhak Mordechai will meet senior Palestinian negotiator Mahmoud 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.

Multiple descriptions per entity

Bill Clinton

U.S. PresidentPresidentAn Arkansas nativeDemocratic presidential candidate

Profile for Bill Clinton

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?– …

IE example

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-1Activity: PRODUCTIONCompany: “Bridgestone Sports Taiwan Co.”Product: “iron and ‘metal wood’ clubs”Start Date: DURING: January 1990

TIE-UP-1Relationship: TIE-UPEntities: “Bridgestone Sport Co.” “a local concern” “a Japanese trading house”Joint Venture Company: “Bridgestone Sports Taiwan Co.”Activity: ACTIVITY-1Amount: NT$200000000

………. 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 BenzProducts: buses and bus chassisLocation: Yangzhou,ChinaCompanies involved: (1)Name: X? Country: German (2)Name: Y? Country: China

Another text

Need another template

Crime-Type: Murder Type: StabbingThe killed: Name: Jurgen Pfrang Age: 51 Profession: Deputy general managerLocation: Nanjing, China

………. 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. ……….

Limitations of IE

• Templates are hand-crafted by human experts

• Templates are domain dependent and not easily portable

• Templates = pre-defined questions

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

Question answering

•TREC Question answering track•Eight years

•Corpus: texts and questions

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.

Type of Answer

QA System Output

AnswerBus Sentences

AskJeeves Documents

IONAUT Passages

LCC Sentences

Mulder Extracted answers

QuASM Document blocks

START Mixture

Webclopedia Sentences

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

Type of QuestionQA-Token Question type Example

PLACE$ Where In the Rocky Mountains

COUNTRY$ Where/What country United Kingdom

STATE$ Where/What state Massachusetts

PERSON$ Who Albert Einstein

ROLE$ Who Doctor

NAME$ Who/What/Which The Shakespeare Festival

ORG$ Who/What The US Post Office

DURATION$ How long For 5 centuries

AGE$ How old 30 years old

YEAR$ When/What year 1999

TIME$ When In the afternoon

DATE$ When/What date July 4th, 1776

VOLUME$ How big 3 gallons

AREA$ How big 4 square inches

LENGTH$ How big/long/high 3 miles

WEIGHT$ How big/heavy 25 tons

NUMBER$ How many 1,234.5

METHOD$ How By rubbing

RATE$ How much 50 per cent

MONEY$ How much 4 million dollars

AnswerBus

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

Text To Speech

• Usually for vision-impaired users

• Demo: http://www.research.att.com/projects/tts/demo.html