Johan Bos November 2005 Carol Beer Little Britain

© Johan Bos November 2005 Computer says “no”

Question Answering • Lecture 1 (Today): Introduction; History of QA; Architecture of a QA system; Evaluation. © Johan Bos November 2005 • Lecture 2 (Friday): Question Classification; NLP techniques for question analysis; POS-tagging; Parsing; Semantic analysis; Word. Net. • Lecture 3 (Next Monday): Retrieving Answers; Document pre-processing; Tokenisation; Stemming; Lemmatisation; Named Entity Recognition; Anaphora Resolution; Matching; Use of knowledge resources; Reranking; Sanity checking.

© Johan Bos November 2005 What is Question Answering? ?

Information Pinpointing © Johan Bos November 2005 Information required: Average number of car accidents per year in Sweden. Two ways of getting this information: - Ask Google or a similar search engine (good luck!) - Ask a QA system the question: What’s the rate of car accidents in Sweden?

QA vs IR • Traditional method for information access: IR (Information Retrieval) © Johan Bos November 2005 – Think of IR as finding the “right book in a library” – Think of QA as a “librarian giving you the book and opening it on the page with the information you’re looking for”

QA vs IE • Traditional method for information access: IE (Information Extraction) © Johan Bos November 2005 – Think of IE as finding answers to a predefined question (i. e. , a template) – Think of QA as asking any question you like

What is Question Answering? • Questions in natural language, not queries! © Johan Bos November 2005 • Answers, not documents!

Why do we need QA? © Johan Bos November 2005 • Information overload problem • Accessing information using traditional methods such as IR and IE are limited • QA increasingly important because: – Size of available information grows – There is duplicate information – There is false information – More and more “computer illiterates” accessing electronically stored information

Information Avalanche • Available information is growing*: – 1999: 250 MB pp for each person on earth – 2002: 800 MB pp for each person on earth © Johan Bos November 2005 • People want specific information * source: M. de Rijke 2005

© Johan Bos November 2005 People ask Questions* * source: M. de Rijke 2005

Why is QA hard? (1/3) © Johan Bos November 2005 • Questions are expressed in natural language (such as English or Italian) • Unlike formal languages, natural languages allow a great deal of flexibility • Example: – – What is the population of Rome? How many people live in Rome? What’s the size of Rome? How many inhabitants does Rome have?

Why is QA hard? (2/3) • Answers are expressed in natural language (such as English or Italian) • Unlike formal languages, natural languages allow a great deal of flexibility • Example: © Johan Bos November 2005 …is estimated at 2. 5 million residents… … current population of Rome is 2817000… …Rome housed over 1 million inhabitants…

Why is QA hard? (3/3) • Answers could be spread across different documents • Examples: © Johan Bos November 2005 – Which European countries produce wine? [Document A contains information about Italy, and document B about France] – What does Bill Clinton’s wife do for a living? [Document A explains that Bill Clinton’s wife is Hillary Clinton, and Document B tells us that she’s a politician]

© Johan Bos November 2005 History of QA (de Rijke & Webber 2003) • QA is by no means a new area! • Simmons (1965) reviews 15 implemented and working systems • Many ingredients of today’s QA systems are rooted in these early approaches • Database oriented systems, domain independent, as opposed to today’s systems that work on large sets of unstructured texts

© Johan Bos November 2005 Examples of early QA systems • BASEBALL (Green et al. 1963) Answers English questions about scores, locations and dates of baseball games • LUNAR (Woods 1977) Accesses chemical data on lunar material compiled during the Apollo missions • PHLIQA 1 (Scha et al. 1980) Answers short questions against a database of computer installations in Europe

Recent work in QA • Since the 1990 s research in QA has by and large focused on open-domain applications © Johan Bos November 2005 • Recently interest in restricted-domain QA has increased, in particular in commercial applications – Banking, entertainment, etc.

Architecture of a QA system corpus question Question Analysis query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Question Analysis • Input: Natural Language Question © Johan Bos November 2005 • Output: Expected Answer Type (Formal) Representation of Question • Techniques used: Machine learning, parsing

Document Analysis • Input: Documents or Passages © Johan Bos November 2005 • Output: (Formal) Representation of Passages that might contain the answer • Techniques used: Tokenisation, Named Entity Recognition, Parsing

Answer Retrieval • Input: Expected Answer Type Question (formal representation) Passages (formal representation) © Johan Bos November 2005 • Output: Ranked list of answers • Techniques used: Matching, Re-ranking, Validation

Example Run corpus question Question Analysis query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run How long is the river Thames? corpus question Question Analysis query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run length river thames corpus question Question Analysis query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run corpus question Question Analysis MEASURE query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run corpus question Question Analysis query Answer(x) &documents/passages length(y, x) & river(y) & named(y, thames) answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run A: NYT 199802 -31 B: APW 199805 -12 C: NYT 200011 -07 corpus question Question Analysis query documents/passages answer-type question representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Example Run A: 30(u) & mile(u) & length(v, u) & river(y) query B: 60(z) & centimeter(z) & Question question height(v, z) & dog(z) Analysis C: 230(u) & kilometer(u) & length(x, u) answer-type & river(x) question representation © Johan Bos November 2005 answers Answer Extraction corpus IR documents/passages Document Analysis passage representation

Example Run corpus question Question Analysis query documents/passages C: 230 kilometer answer-type A: 30 miles B: 60 question centimeter representation © Johan Bos November 2005 answers IR Answer Extraction Document Analysis passage representation

Evaluating QA systems • International evaluation campaigns for QA systems (open domain QA): © Johan Bos November 2005 – TREC (Text Retrieval Conference) http: //trec. nist. gov/ – CLEF (Cross Language Evaluation Forum) http: //clef-qa. itc. it/ – NTCIR (NII Test Collection for IR Systems) http: //www. slt. atr. jp/CLQA/

TREC-QA (organised by NIST) • Annual event, started in 1999 • Difficulty of the QA task increased over the years: – 1999: Answers in snippets, ranked list of answers; – 2005: Exact answers, only one answer. © Johan Bos November 2005 • Three types of questions: – Factoid questions – List questions – Definition questions

QA@CLEF • CLEF is the “European edition” of TREC • Monolingual (non-English) QA – Bulgarian (BG), German (DE), Spanish (ES), Finnish (FI), French (FR), Italian (IT), Dutch (NL), Portuguese (PT) • Cross-Lingual QA © Johan Bos November 2005 – Questions posed in source language, answer searched in documents of target language – All combinations possible

Open-Domain QA • QA at TREC is considered “Open-Domain” QA – Document collection is Acquint Corpus (over a million documents) – Questions can be about anything © Johan Bos November 2005 • Restricted-Domain QA – Documents described a specific domain – Detailed questions – Less redundancy of answers!

TREC-type questions • Factoid questions – Where is the Taj Mahal? • List questions – What actors have played Tevye in `Fiddler on the Roof'? © Johan Bos November 2005 • Definition/biographical questions – What is a golden parachute? – Who is Vlad the Impaler?

What is a correct answer? • Example Factoid Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: – Kafka died in 1923. – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

What is a correct answer? • Example Factoid Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: Incorrect – Kafka died in 1923. – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

What is a correct answer? • Example Factoid Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: Inexact (under-informative) – Kafka died in 1923. – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

What is a correct answer? • Example Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: Inexact (over-informative) – Kafka died in 1923. – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

What is a correct answer? • Example Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: – Kafka died in 1923. Unsupported – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

What is a correct answer? • Example Question – When did Franz Kafka die? © Johan Bos November 2005 • Possible Answers: Correct – Kafka died in 1923. – Kafka died in 1924. – Kafka died on June 3, 1924 from complications related to Tuberculosis. – Ernest Watz was born June 3, 1924. – Kafka died on June 3, 1924.

Answer Accuracy © Johan Bos November 2005 # correct answers Answer Accuracy = -------------#

Correct answers to list questions Example List Question Which European countries produce wine? System A: © Johan Bos November 2005 France Italy System B: Scotland France Germany Italy Spain Iceland Greece the Netherlands Japan Turkey Estonia

Evaluation metrics for list questions • Precision (P): # answers judged correct & distinct P = -----------------------# answers returned © Johan Bos November 2005 • Recall (R): • F-Score (F): # answers judged correct & distinct R = ------------------------# total answers 2*P*R F = ------P+R

Correct answers to list questions Example List Question Which European countries produce wine? System A: © Johan Bos November 2005 France Italy P = 1. 00 R = 0. 25 F = 0. 40 System B: Scotland France Germany Italy Spain Iceland Greece the Netherlands P = 0. 64 Japan R = 0. 88 Turkey F = 0. 74 Estonia

Other evaluation metrics System A: Ranked answers (Accuracy = 0. 2) Q 1 Q 2 Q 3 Q 4 Q 6 Q 7 Q 8 Q 9 …. Qn A 1 W W C W W W …. W A 2 W W W W …. W A 3 W W W W …. W A 4 W W W W …. W A 5 W C W W …. W © Johan Bos November 2005 System B: Ranked answers (Accuracy = 0. 1) Q 1 Q 2 Q 3 Q 4 Q 6 Q 7 Q 8 Q 9 …. Qn A 1 W W C W W W …. W A 2 C W W C C W …. C A 3 W C W W W …. W A 4 W W W C W W …. W A 5 W W W W …. W

Mean Reciprocal Rank (MRR) • Score for an individual question: – The reciprocal of the rank at which the first correct answer is returned – 0 if no correct response is returned • The score for a run: © Johan Bos November 2005 – Mean over the set of questions in the test

MRR in action System A: MRR = (. 2+1+1+. 2)/10 = 0. 24 Q 1 Q 2 Q 3 Q 4 Q 6 Q 7 Q 8 Q 9 …. Qn A 1 W W C W W W …. W A 2 W W W W …. W A 3 W W W W …. W A 4 W W W W …. W A 5 W C W W …. W © Johan Bos November 2005 System B: MRR = (. 5+. 33+. 5+. 25+1+. 5+. 5)/10=0. 42 Q 1 Q 2 Q 3 Q 4 Q 6 Q 7 Q 8 Q 9 …. Qn A 1 W W C W W W …. W A 2 C W W C C W …. C A 3 W C W W W …. W A 4 W W W C W W …. W A 5 W W W W …. W

Open-Domain Question Answering • TREC QA Track – Factoid questions – List questions – Definition questions © Johan Bos November 2005 • State-of-the-Art – Hard problem – Only few systems with good results

Friday • QA Lecture 2: © Johan Bos November 2005 – – – Question Classification NLP techniques for question analysis POS-tagging Parsing Semantic analysis Use of lexical resources such as Word. Net

© Johan Bos November 2005 Question Classification (preview) • • • • • How many islands does Italy have? When did Inter win the Scudetto? What are the colours of the Lithuanian flag? Where is St. Andrews located? Why does oil float in water? How did Frank Zappa die? Name the Baltic countries. Which seabird was declared extinct in the 1840 s? Who is Noam Chomsky? List names of Russian composers. Edison is the inventor of what? How far is the moon from the sun? What is the distance from New York to Boston? How many planets are there? What is the exchange rate of the Euro to the Dollar? What does SPQR stand for? What is the nickname of Totti? What does the Scottish word “bonnie” mean? Who wrote the song “Paranoid Android”?