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Slide1: 

Open Domain Question Answering: Techniques, Resources and Systems Bernardo Magnini Itc-Irst Trento, Italy magnini@itc.it

Outline of the Tutorial: 

Outline of the Tutorial Introduction to QA QA at TREC System Architecture - Question Processing - Answer Extraction Answer Validation on the Web Cross-Language QA

Previous Lectures/Tutorials on QA: 

Previous Lectures/Tutorials on QA Dan Moldovan and Sanda Harabagiu: Question Answering, IJCAI 2001. Marteen de Rijke and Bonnie Webber: Knowlwdge-Intensive Question Answering, ESSLLI 2003. Jimmy Lin and Boris Katz, Web-based Question Answering, EACL 2004

Introduction to Question Answering: 

Introduction to Question Answering What is Question Answering Applications Users Question Types Answer Types Evaluation Presentation Brief history

Query Driven vs Answer Driven Information Access: 

Query Driven vs Answer Driven Information Access What does LASER stand for? When did Hitler attack Soviet Union? Using Google we find documents containing the question itself, no matter whether or not the answer is actually provided. Current information access is query driven. Question Answering proposes an answer driven approach to information access.

Question Answering: 

Question Answering Find the answer to a question in a large collection of documents questions (in place of keyword-based query) answers (in place of documents)

Why Question Answering?: 

Searching for: Etna Where is Naxos? Searching for: Naxos What continent is Taormina in? What is the highest volcano in Europe? Why Question Answering? Document collection Searching for: Taormina

Alternatives to Information Retrieval: 

Document Retrieval users submit queries corresponding to their information need system returns (voluminous) list of full-length documents it is the responsibility of the users to find their original information need, within the returned documents Open-Domain Question Answering (QA) users ask fact-based, natural language questions What is the highest volcano in Europe? system returns list of short answers … Under Mount Etna, the highest volcano in Europe, perches the fabulous town … more appropriate for specific information needs Alternatives to Information Retrieval

What is QA?: 

What is QA? Find the answer to a question in a large collection of documents What is the brightest star visible from Earth? 1. Sirio A is the brightest star visible from Earth even if it is… 2. the planet is 12-times brighter than Sirio, the brightest star in the sky…

QA: a Complex Problem (1): 

QA: a Complex Problem (1) Problem: discovery implicit relations among question and answers Who is the author of the “Star Spangled Banner”? …Francis Scott Key wrote the “Star Spangled Banner” in 1814. …comedian-actress Roseanne Barr sang her famous rendition of the “Star Spangled Banner” before …

QA: a Complex Problem (2): 

QA: a Complex Problem (2) Problem: discovery implicit relations among question and answers Which is the Mozart birth date? …. Mozart (1751 – 1791) ….

QA: a complex problem (3): 

QA: a complex problem (3) Problem: discovery implicit relations among question and answers Which is the distance between Naples and Ravello? “From the Naples Airport follow the sign to Autostrade (green road sign). Follow the directions to Salerno (A3). Drive for about 6 Km. Pay toll (Euros 1.20). Drive appx. 25 Km. Leave the Autostrade at Angri (Uscita Angri). Turn left, follow the sign to Ravello through Angri. Drive for about 2 Km. Turn right following the road sign "Costiera Amalfitana". Within 100m you come to traffic lights prior to narrow bridge. Watch not to miss the next Ravello sign, at appx. 1 Km from the traffic lights. Now relax and enjoy the views (follow this road for 22 Km). Once in Ravello ...”.

QA: Applications (1): 

QA: Applications (1) Information access: Structured data (databases) Semi-structured data (e.g. comment field in databases, XML) Free text To search over: The Web Fixed set of text collection (e.g. TREC) A single text (reading comprehension evaluation)

QA: Applications (2): 

QA: Applications (2) Domain independent QA Domain specific (e.g. help systems) Multi-modal QA Annotated images Speech data

QA: Users : 

QA: Users Casual users, first time users Understand the limitations of the system Interpretation of the answer returned Expert users Difference between novel and already provided information User Model

QA: Questions (1): 

QA: Questions (1) Classification according to the answer type Factual questions (What is the larger city …) Opinions (What is the author attitude …) Summaries (What are the arguments for and against…) Classification according to the question speech act: Yes/NO questions (Is it true that …) WH questions (Who was the first president …) Indirect Requests (I would like you to list …) Commands (Name all the presidents …)

QA: Questions (2): 

QA: Questions (2) Difficult questions Why, How questions require understanding causality or instrumental relations What questions have little constraint on the answer type (e.g. What did they do?)

QA: Answers: 

QA: Answers Long answers, with justification Short answers (e.g. phrases) Exact answers (named entities) Answer construction: Extraction: cut and paste of snippets from the original document(s) Generation: from multiple sentences or documents QA and summarization (e.g. What is this story about?)

QA: Information Presentation: 

QA: Information Presentation Interfaces for QA Not just isolated questions, but a dialogue Usability and user satisfaction Critical situations Real time, single answer Dialog-based interaction Speech input Conversational access to the Web

QA: Brief History (1): 

QA: Brief History (1) NLP interfaces to databases: BASEBALL (1961), LUNAR (1973), TEAM (1979), ALFRESCO (1992) Limitations: structured knowledge and limited domain Story comprehension: Shank (1977), Kintsch (1998), Hirschman (1999)

QA: Brief History (2): 

QA: Brief History (2) Information retrieval (IR) Queries are questions List of documents are answers QA is close to passage retrieval Well established methodologies (i.e. Text Retrieval Conferences TREC) Information extraction (IE): Pre-defined templates are questions Filled template are answers

Slide22: 

Research Context (1) Growing interest in QA (TREC, CLEF, NT evaluation campaign). Recent focus on multilinguality and context aware QA

Slide23: 

Research Context (2) faithfulness compactness Automatic Summarization Machine Translation Automatic Question Answering as compact as possible answers must be faithful w.r.t. questions (correctness) and compact (exactness) as faithful as possible

II. Question Answering at TREC: 

II. Question Answering at TREC The problem simplified Questions and answers Evaluation metrics Approaches

The problem simplified: The Text Retrieval Conference: 

The problem simplified: The Text Retrieval Conference Goal Encourage research in information retrieval based on large-scale collections Sponsors NIST: National Institute of Standards and Technology ARDA: Advanced Research and Development Activity DARPA: Defense Advanced Research Projects Agency Since 1999 Participants are research institutes, universities, industries

TREC Questions: 

TREC Questions Q-1391: How many feet in a mile? Q-1057: Where is the volcano Mauna Loa? Q-1071: When was the first stamp issued? Q-1079: Who is the Prime Minister of Canada? Q-1268: Name a food high in zinc. Q-896: Who was Galileo? Q-897: What is an atom? Q-711: What tourist attractions are there in Reims? Q-712: What do most tourists visit in Reims? Q-713: What attracts tourists in Reims Q-714: What are tourist attractions in Reims? Fact-based, short answer questions Definition questions Reformulation questions

Answer Assessment: 

Answer Assessment Criteria for judging an answer Relevance: it should be responsive to the question Correctness: it should be factually correct Conciseness: it should not contain extraneous or irrelevant information Completeness: it should be complete, i.e. partial answer should not get full credit Simplicity: it should be simple, so that the questioner can read it easily Justification: it should be supplied with sufficient context to allow a reader to determine why this was chosen as an answer to the question

Slide28: 

Questions at TREC

Exact Answers: 

Exact Answers Basic unit of a response: [answer-string, docid] pair An answer string must contain a complete, exact answer and nothing else. What is the longest river in the United States? The following are correct, exact answers Mississippi, the Mississippi, the Mississippi River, Mississippi River mississippi while none of the following are correct exact answers At 2,348 miles the Mississippi River is the longest river in the US. 2,348 miles; Mississippi Missipp Missouri

Assessments: 

Assessments Four possible judgments for a triple [ Question, document, answer ] Rigth: the answer is appropriate for the question Inexact: used for non complete answers Unsupported: answers without justification Wrong: the answer is not appropriate for the question

Slide31: 

R 1530 XIE19990325.0298 Wellington R 1490 NYT20000913.0267 Albert DeSalvo R 1503 XIE19991018.0249 New Guinea U 1402 NYT19981017.0283 1962 R 1426 NYT19981030.0149 Sundquist U 1506 NYT19980618.0245 Excalibur R 1601 NYT19990315.0374 April 18 , 1955 X 1848 NYT19991001.0143 Enola R 1838 NYT20000412.0164 Fala R 1674 APW19990717.0042 July 20 , 1969 X 1716 NYT19980605.0423 Barton R 1473 APW19990826.0055 1908 R 1622 NYT19980903.0086 Ellen W 1510 NYT19980909.0338 Young Girl R=Right, X=ineXact, U=Unsupported, W=Wrong What is the capital city of New Zealand? What is the Boston Strangler's name? What is the world's second largest island? What year did Wilt Chamberlain score 100 points? Who is the governor of Tennessee? What's the name of King Arthur's sword? When did Einstein die? What was the name of the plane that dropped the Atomic Bomb on Hiroshima? What was the name of FDR's dog? What day did Neil Armstrong land on the moon? Who was the first Triple Crown Winner? When was Lyndon B. Johnson born? Who was Woodrow Wilson's First Lady? Where is Anne Frank's diary?

Slide32: 

1402: What year did Wilt Chamberlain score 100 points? DIOGENE: 1962 ASSESMENT: UNSUPPORTED PARAGRAPH: NYT19981017.0283 Petty's 200 victories, 172 of which came during a 13-year span between 1962-75, may be as unapproachable as Joe DiMaggio's 56-game hitting streak or Wilt Chamberlain's 100-point game.

Slide33: 

1506: What's the name of King Arthur's sword? ANSWER: Excalibur PARAGRAPH: NYT19980618.0245 ASSESMENT: UNSUPPORTED `QUEST FOR CAMELOT,' with the voices of Andrea Carr, Gabriel Byrne, Cary Elwes, John Gielgud, Jessalyn Gilsig, Eric Idle, Gary Oldman, Bronson Pinchot, Don Rickles and Bryan White. Directed by Frederik Du Chau (G, 100 minutes). Warner Brothers' shaky entrance into the Disney-dominated sweepstakes of the musicalized animated feature wants to be a juvenile feminist ``Lion King'' with a musical heart that fuses ``Riverdance'' with formulaic Hollywood gush. But its characters are too wishy-washy and visually unfocused to be compelling, and the songs (by David Foster and Carole Bayer Sager) so forgettable as to be extraneous. In this variation on the Arthurian legend, a nondescript Celtic farm girl named Kayley with aspirations to be a knight wrests the magic sword Excalibur from the evil would-be emperor Ruber (a Hulk Hogan look-alike) and saves the kingdom (Holden).

Slide34: 

1848: What was the name of the plane that dropped the Atomic Bomb on Hiroshima? DIOGENE: Enola PARAGRAPH: NYT19991001.0143 ASSESMENT: INEXACT Tibbets piloted the Boeing B-29 Superfortress Enola Gay, which dropped the atomic bomb on Hiroshima on Aug. 6, 1945, causing an estimated 66,000 to 240,000 deaths. He named the plane after his mother, Enola Gay Tibbets.

Slide35: 

1716: Who was the first Triple Crown Winner? DIOGENE: Barton PARAGRAPH: NYT19980605.0423 ASSESMENT: INEXACT Not all of the Triple Crown winners were immortals. The first, Sir Barton, lost six races in 1918 before his first victory, just as Real Quiet lost six in a row last year. Try to find Omaha and Whirlaway on anybody's list of all-time greats.

Slide36: 

1510: Where is Anne Frank's diary? DIOGENE: Young Girl PARAGRAPH: NYT19980909.0338 ASSESMENT: WRONG Otto Frank released a heavily edited version of “B” for its first publication as “Anne Frank: Diary of a Young Girl” in 1947.

TREC Evaluation Metric: Mean Reciprocal Rank (MRR): 

TREC Evaluation Metric: Mean Reciprocal Rank (MRR) Reciprocal Rank = inverse of rank at which first correct answer was found: [1, 0,5, 0.33, 0.25, 0.2, 0] MRR: average over all questions Strict score: unsupported count as incorrect Lenient score: unsupported count as correct

TREC Evaluation Metrics: Confidence-Weighted Score (CWS): 

TREC Evaluation Metrics: Confidence-Weighted Score (CWS) Sum for i = 1 to 500 (#-correct-up-to-question i / i) 500 System A: 1  C 2  W 3  C 4  C 5  W System B: 1  W 2  W 3  C 4  C 5  C (1/1) + ((1+0)/2) + (1+0+1)/3) + ((1+0+1+1)/4) + ((1+0+1+1+0)/5) 5 Total: 0.7 0 + ((0+0)/2) + (0+0+1)/3) + ((0+0+1+1)/4) + ((0+0+1+1+1)/5) 5 Total: 0.29

Evaluation: 

Evaluation Best result: 67% Average over 67 runs: 23%

Main Approaches at TREC: 

Main Approaches at TREC Knowledge-Based Web-based Pattern-based

Knowledge-Based Approach: 

Knowledge-Based Approach Linguistic-oriented methodology Determine the answer type from question form Retrieve small portions of documents Find entities matching the answer type category in text snippets Majority of systems use a lexicon (usually WordNet) To find answer type To verify that a candidate answer is of the correct type To get definitions Complex architecture...

Web-Based Approach: 

Web-Based Approach QUESTION

Patter-Based Approach (1/3): 

Patter-Based Approach (1/3) Knowledge poor Strategy Search for predefined patterns of textual expressions that may be interpreted as answers to certain question types. The presence of such patterns in answer string candidates may provide evidence of the right answer.

Patter-Based Approach (2/3): 

Patter-Based Approach (2/3) Conditions Detailed categorization of question types Up to 9 types of the “Who” question; 35 categories in total Significant number of patterns corresponding to each question type Up to 23 patterns for the “Who-Author” type, average of 15 Find multiple candidate snippets and check for the presence of patterns (emphasis on recall)

Pattern-based approach (3/3): 

Pattern-based approach (3/3) Example: patterns for definition questions Question: What is A? 1. <A; is/are; [a/an/the]; X> ...23 correct answers 2. <A; comma; [a/an/the]; X; [comma/period]> …26 correct answers 3. <A; [comma]; or; X; [comma]> …12 correct answers 4. <A; dash; X; [dash]> …9 correct answers 5. <A; parenthesis; X; parenthesis> …8 correct answers 6. <A; comma; [also] called; X [comma]> …7 correct answers 7. <A; is called; X> …3 correct answers total: 88 correct answers

Use of answer patterns: 

Use of answer patterns For generating queries to the search engine. How did Mahatma Gandhi die? Mahatma Gandhi die <HOW> Mahatma Gandhi die of <HOW> Mahatma Gandhi lost his life in <WHAT> The TEXTMAP system (ISI) uses 550 patterns, grouped in 105 equivalence blocks. On TREC-2003 questions, the system produced, on average, 5 reformulations for each question. For answer extraction When was Mozart born? P=1 <PERSON> (<BIRTHDATE> - DATE) P=.69 <PERSON> was born on <BIRTHDATE>

Acquisition of Answer Patterns: 

Acquisition of Answer Patterns Relevant approaches: Manually developed surface pattern library (Soubbotin, Soubbotin, 2001) Automatically extracted surface patterns (Ravichandran, Hovy 2002) Patter learning: Start with a seed, e.g. (Mozart, 1756) Download Web documents using a search engine Retain sentences that contain both question and answer terms Construct a suffix tree for extracting the longest matching substring that spans <Question> and <Answer> Calculate precision of patterns Precision = # of correct patterns with correct answer / # of total patterns

Capturing variability with patterns: 

Capturing variability with patterns Pattern based QA is more effective when supported by variable typing obtained using NLP techniques and resources. When was <A> born? <A:PERSON> (<ANSWER:DATE> - <A :PERSON > was born in <ANSWER :DATE > Surface patterns can not deal with word reordering and apposition phrases: Galileo, the famous astronomer, was born in … The fact that most of the QA systems use syntactic parsing demonstrates that the successful solution of the answer extraction problem goes beyond the surface form analysis

Syntactic answer patterns (1): 

Syntactic answer patterns (1) Answer patterns that capture the syntactic relations of a sentence. When was <A> invented?

Syntactic answer patterns (2): 

Syntactic answer patterns (2) S NP VP The first was invented PP in 1877 phonograph The matching phase turns out to be a problem of partial match among syntactic trees.

III. System Architecture: 

III. System Architecture Knowledge Based approach Question Processing Search component Answer Extraction

Knowledge based QA: 

Knowledge based QA Search Component ANSWER ANSWER IDENTIFICATION

Question Analysis (1): 

Question Analysis (1) Input: NLP question Output: query for the search engine (i.e. a boolean composition of weighted keywords) Answer type Additional constraints: question focus, syntactic or semantic relations that should hold for a candidate answer entity and other entities

Question Analysis (2): 

Question Analysis (2) Steps: Tokenization POS-tagging Multi-words recognition Parsing Answer type and focus identification Keyword extraction Word Sense Disambiguation Expansions

Tokenization and POS-tagging: 

Tokenization and POS-tagging NL-QUESTION: Who was the inventor of the electric light? Who Who CCHI [0,0] was be VIY [1,1] the det RS [2,2] inventor inventor SS [3,3] of of ES [4,4] the det RS [5,5] electric electric AS [6,6] light light SS [7,7] ? ? XPS [8,8]

Multi-Words recognition: 

Multi-Words recognition NL-QUESTION: Who was the inventor of the electric light? Who Who CCHI [0,0] was be VIY [1,1] the det RS [2,2] inventor inventor SS [3,3] of of ES [4,4] the det RS [5,5] electric_light electric_light SS [6,7] ? ? XPS [8,8]

Syntactic Parsing: 

Syntactic Parsing Identify syntactic structure of a sentence noun phrases (NP), verb phrases (VP), prepositional phrases (PP) etc. Why did David Koresh ask the FBI for a word processor WRB VBD NNP NNP VB DT NNP IN DT NN NN WHADVP NP NP NP PP VP SQ SBARQ Why did David Koresh ask the FBI for a word processor?

Answer Type and Focus: 

Answer Type and Focus Focus is the word that expresses the relevant entity in the question Used to select a set of relevant documents ES: Where was Mozart born? Answer Type is the category of the entity to be searched as answer PERSON, MEASURE, TIME PERIOD, DATE, ORGANIZATION, DEFINITION ES: Where was Mozart born? LOCATION

Answer Type and Focus: 

Answer Type and Focus What famous communist leader died in Mexico City? RULENAME: WHAT-WHO TEST: [“what” [¬ NOUN]* [NOUN:person-p]J +] OUTPUT: [“PERSON” J] Answer type: PERSON Focus: leader This rule matches any question starting with what, whose first noun, if any, is a person (i.e. satisfies the person-p predicate)

Keywords Extraction: 

Keywords Extraction NL-QUESTION: Who was the inventor of the electric light? Who Who CCHI [0,0] was be VIY [1,1] the det RS [2,2] inventor inventor SS [3,3] of of ES [4,4] the det RS [5,5] electric_light electric_light SS [6,7] ? ? XPS [8,8]

Word Sense Disambiguation: 

Word Sense Disambiguation What is the brightest star visible from Earth?” STAR star#1: celestial body ASTRONOMY star#2: an actor who play … ART BRIGHT bright #1: bright brilliant shining PHYSICS bright #2: popular glorious GENERIC bright #3: promising auspicious GENERIC VISIBLE visible#1: conspicuous obvious PHYSICS visible#2: visible seeable ASTRONOMY EARTH earth#1: Earth world globe ASTRONOMY earth #2: estate land landed_estate acres ECONOMY earth #3: clay GEOLOGY earth #4: dry_land earth solid_ground GEOGRAPHY earth #5: land ground soil GEOGRAPHY earth #6: earth ground GEOLOGY

Expansions: 

Expansions - NL-QUESTION: Who was the inventor of the electric light? - BASIC-KEYWORDS: inventor electric-light inventor synonyms discoverer, artificer derivation invention synonyms innovation derivation invent synonyms excogitate electric_light synonyms incandescent_lamp, ligth_bulb

Keyword Composition: 

Keyword Composition Keywords and expansions are composed in a boolean expression with AND/OR operators Several possibilities: AND composition Cartesian composition (OR (inventor AND electric_light) OR (inventor AND incandescent_lamp) OR (discoverer AND electric_light) ………………………… OR inventor OR electric_light))

Document Collection Pre-processing: 

Document Collection Pre-processing For real time QA applications off-line pre-processing of the text is necessary Term indexing POS-tagging Named Entities Recognition

Candidate Answer Document Selection: 

Candidate Answer Document Selection Passage Selection: Individuate relevant, small, text portions Given a document and a list of keywords: Paragraph length (e.g. 200 words) Consider the percentage of keywords present in the passage Consider if some keyword is obligatory (e.g. the focus of the question).

Candidate Answer Document Analysis: 

Candidate Answer Document Analysis Passage text tagging Named Entity Recognition Who is the author of the “Star Spangled Banner”? …<PERSON>Francis Scott Key </PERSON> wrote the “Star Spangled Banner” in <DATE>1814</DATE> Some systems: passages parsing (Harabagiu, 2001) Logical form (Zajac, 2001)

Answer Extraction (1): 

Answer Extraction (1) Who is the author of the “Star Spangled Banner”? …<PERSON>Francis Scott Key </PERSON> wrote the “Star Spangled Banner” in <DATE>1814</DATE> Answer Type = PERSON Candidate Answer = Francis Scott Key Ranking candidate answers: keyword density in the passage, apply additional constraints (e.g. syntax, semantics), rank candidates using the Web

Answer Identification: 

Answer Identification Thomas E. Edison

IV. Answer Validation: 

IV. Answer Validation Automatic answer validation Approach: web-based use of patterns combine statistics and linguistic information Discussion Conclusions

QA Architecture: 

QA Architecture Search Component ANSWER Answer Extraction Component ANSWER RANKING NAMED ENTITIES RECOGNITION PARAGRAPH FILTERING ANSWER IDENTIFICATION QUERY COMPOSITION SEARCH ENGINE Document collection TOKENIZATION & POS TAGGING

The problem: Answer Validation: 

The problem: Answer Validation Given a question q and a candidate answer a, decide if a is a correct answer for q What is the capital of the USA? Washington D.C. San Francisco Rome

The problem: Answer Validation: 

The problem: Answer Validation Given a question q and a candidate answer a, decide if a is a correct answer for q What is the capital of the USA? Washington D.C. correct San Francisco wrong Rome wrong

Requirements for Automatic AV: 

Requirements for Automatic AV Accuracy: it has to compare well with respect to human judgments Efficiency: large scale (Web), real time scenarios Simplicity: avoid the complexity of QA systems

Approach: 

Approach Web-based take advantage of Web redundancy Pattern-based the Web is mined using patterns (i.e. validation patterns) extracted from the question and the candidate answer Quantitative (as opposed to content-based) check if the question and the answer tend to appear together in the Web considering the number of documents returned (i.e. documents are not downloaded)

Web Redundancy: 

Web Redundancy What is the capital of the USA? Washington

Validation Pattern: 

Validation Pattern [Capital NEAR USA NEAR Washington]

Related Work: 

Related Work Pattern-based QA Brill, 2001 – TREC-10 Subboting, 2001 – TREC-10 Ravichandran and Hovy, ACL-02 Use of the Web for QA Clarke et al. 2001 – TREC-10 Radev, et al. 2001 - CIKM Statistical approach on the Web PMI-IR: Turney, 2001 and ACL-02

Architecture: 

Architecture > t < t #doc #doc < k

Architecture: 

Architecture validation pattern > t < t #doc #doc < k

Extracting Validation Patterns: 

Extracting Validation Patterns question pattern (Qp) answer pattern (Ap) stop-word filter term expansion answer type named entity recognition stop-word filter

Architecture: 

Architecture answer validity score > t < t #doc #doc < k

Answer Validity Score: 

Answer Validity Score PMI-IR algorithm (Turney, 2001) The result is interpreted as evidence that the validation pattern is consistent, which imply answer accuracy

Answer Validity Score: 

Answer Validity Score PMI (Qp, Ap) = hits(Qp NEAR Ap) hits(Qp) * hits(Ap) Three searches are submitted to the Web: hits(Qp) hits(Ap) hits(Qp NEAR Ap)

Example: 

Example What county is Modesto, California in? Answer type: Location Qp = [county NEAR Modesto NEAR California] P(Qp) = P(county, Modesto, California) = 909 A1= The Stanislaus County district attorney’s A2 = In Modesto, San Francisco, and Stop-word filter

Example (cont.): 

Example (cont.) The Stanislaus County district attorney’s A1p = [Stanislaus] In Modesto, San Francisco, and A2p = [San Francisco] P(Stanislaus)= 73641 P(San Francisco)= 4072519 NER(location)

Example (cont.): 

Example (cont.) PMI(Qp, A1p) = 2473 PMI(Qp, A2p) = 0.89 The Stanislaus County district attorney’s In Modesto, San Francisco, and correct answer wrong answer t = 0.2 * MAX(AVS) > t < t

Experiments: 

Experiments Data set: 492 TREC-2001 questions 2726 answers: 3 correct answers and 3 wrong answers for each question, randomly selected from TREC-10 participants human-judged corpus Search engine: Altavista allows the NEAR operator

Experiment: Answers: 

Experiment: Answers Q-916: What river in the US is known as the Big Muddy ?

Baseline: 

Baseline Consider the documents provided by NIST to TREC-10 participants (1000 documents for each question) If the candidate answer occurs (i.e. string match) at least one time in the top 10 documents it is judged correct, otherwise it is considered wrong

Asymmetrical Measures: 

Asymmetrical Measures Problem: some candidate answers (e.g. numbers) produce an enormous amount of Web documents Scores for good (Ac) and bad (Aw) answers tend to be similar, making the choice more difficult How many Great Lakes are there? … to cross all five Great Lakes completed a 19.2 …

Asymmetric Conditional Probability (ACP): 

Asymmetric Conditional Probability (ACP) ACP (Qsp, Asp) = P(Qsp | Asp) P(Qsp) * P(Asp) = hits(Qsp NEAR Asp) hits(Qsp) * hits(Asp) 2/3 2/3

Comparing PMI and ACP: 

Comparing PMI and ACP ACP increases the difference between the right and the wrong answer.

Results: 

Results SR on all 492 TREC-2001 questions SR on all 249 factoid questions

Discussion (1): 

Discussion (1) Definition questions are the more problematic on the subset of 249 named-entities questions success rate is higher (i.e. 86.3) Relative threshold improve performance (+ 2%) over fixed threshold Non symmetric measures of co-occurrence work better for answer validation (+ 2%) Source of errors: Answer type recognition Named-entities recognition TREC answer set (e.g. tokenization)

Discussion (2): 

Discussion (2) Automatic answer validation is a key challenge for Web-based question/answering systems Requirements: accuracy with respect to human judgments: 80% success rate is a good starting point efficiency: documents are not downloaded simplicity: based on patterns At present, it is suitable for a generate&test component integrated in a QA system

V. Cross-Language QA: 

V. Cross-Language QA Motivations QA@CLEF Performances Approaches

Motivations: 

Motivations Answers may be found in languages different from the language of the question. Interest in QA systems for languages other than English. Force the QA community to design real multilingual systems. Check/improve the portability of the technologies implemented in current English QA systems.

Cross-Language QA: 

Cross-Language QA Quanto è alto il Mont Ventoux? (How tall is Mont Ventoux?) “Le Mont Ventoux, impérial avec ses 1909 mètres et sa tour blanche telle un étendard, règne de toutes …” 1909 metri English corpus Italian corpus Spanish corpus French corpus

CL-QA at CLEF: 

CL-QA at CLEF Adopt the same rules used at TREC QA Factoid questions (i.e. no definition questions) Exact answers + document id Use the CLEF corpora (news, 1994 -1995) Return the answer in the language of the text collection in which it has been found (i.e. no translation of the answer) QA-CLEF-2003 was an initial step toward a more complex task organized at CLEF-2004 and 2005.

Slide100: 

QA @ CLEF 2004 (http://clef-qa.itc.it/2004) Seven groups coordinated the QA track: ITC-irst (IT and EN test set preparation) DFKI (DE) ELDA/ELRA (FR) Linguateca (PT) UNED (ES) U. Amsterdam (NL) U. Limerick (EN assessment) Two more groups participated in the test set construction: Bulgarian Academy of Sciences (BG) U. Helsinki (FI)

Slide101: 

CLEF QA - Overview

Slide102: 

CLEF QA – Task Definition Given 200 questions in a source language, find one exact answer per question in a collection of documents written in a target language, and provide a justification for each retrieved answer (i.e. the docid of the unique document that supports the answer). S T 6 monolingual and 50 bilingual tasks. Teams participated in 19 tasks,

Slide103: 

CLEF QA - Questions All the test sets were made up of 200 questions: ~90% factoid questions ~10% definition questions ~10% of the questions did not have any answer in the corpora (right answer-string was “NIL”) Problems in introducing definition questions: What’s the right answer? (it depends on the user’s model) What’s the easiest and more efficient way to assess their answers? Overlap with factoid questions: F Who is the Pope? D Who is John Paul II? the Pope John Paul II the head of the Roman Catholic Church

Slide104: 

CLEF QA – Multieight <q cnt="0675" category="F" answer_type="MANNER"> <language val="BG" original="FALSE"> <question group="BTB">Как умира Пазолини?</question> <answer n="1" docid="">TRANSLATION[убит]</answer> </language> <language val="DE" original="FALSE"> <question group="DFKI">Auf welche Art starb Pasolini?</question> <answer n="1" docid="">TRANSLATION[ermordet]</answer> <answer n="2" docid="SDA.951005.0154">ermordet</answer> </language> <language val="EN" original="FALSE"> <question group="LING">How did Pasolini die?</question> <answer n="1" docid="">TRANSLATION[murdered]</answer> <answer n="2" docid="LA112794-0003">murdered</answer> </language> <language val="ES" original="FALSE"> <question group="UNED">¿Cómo murió Pasolini?</question> <answer n="1" docid="">TRANSLATION[Asesinado]</answer> <answer n="2" docid="EFE19950724-14869">Brutalmente asesinado en los arrabales de Ostia</answer> </language> <language val="FR" original="FALSE">   <question group="ELDA">Comment est mort Pasolini ?</question>   <answer n="1" docid="">TRANSLATION[assassiné]</answer>   <answer n="2" docid="ATS.951101.0082">assassiné</answer>   <answer n="3" docid="ATS.950904.0066">assassiné en novembre 1975 dans des circonstances mystérieuses</answer>   <answer n="4" docid="ATS.951031.0099">assassiné il y a 20 ans</answer> </language> <language val="IT" original="FALSE">   <question group="IRST">Come è morto Pasolini?</question>   <answer n="1" docid="">TRANSLATION[assassinato]</answer>   <answer n="2" docid="AGZ.951102.0145">massacrato e abbandonato sulla spiaggia di Ostia</answer>   </language> <language val="NL" original="FALSE">   <question group="UoA">Hoe stierf Pasolini?</question>   <answer n="1" docid="">TRANSLATION[vermoord]</answer>   <answer n="2" docid="NH19951102-0080">vermoord</answer>   </language> <language val="PT" original="TRUE">   <question group="LING">Como morreu Pasolini?</question>   <answer n="1" docid="LING-951120-088">assassinado</answer>   </language> </q>

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CLEF QA - Assessment Judgments taken from the TREC QA tracks: Right Wrong ineXact Unsupported Other criteria, such as the length of the answer-strings (instead of X, which is underspecified) or the usefulness of responses for a potential user, have not been considered. Main evaluation measure was accuracy (fraction of Right responses). Whenever possible, a Confidence-Weighted Score was calculated:

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Evaluation Exercise - Participants Distribution of participating groups in different QA evaluation campaigns.

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Evaluation Exercise - Participants Number of participating teams-number of submitted runs at CLEF 2004. Comparability issue.

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Evaluation Exercise - Results Systems’ performance at the TREC and CLEF QA tracks. * considering only the 413 factoid questions ** considering only the answers returned at the first rank 70 25 65 24 67 23 70 21.4 41.5 29 35 17 45.5 23.7 35 14.7 accuracy (%) TREC-8 TREC-9 TREC-10 TREC-11 TREC-12* CLEF-2003** monol. bil. CLEF-2004 monol. bil. best system average

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Evaluation Exercise – CL Approaches Question Analysis / keyword extraction INPUT (source language) Candidate Document Selection Document Collection Document Collection Preprocessing Preprocessed Documents Candidate Document Analysis Answer Extraction OUTPUT (target language) question translation into target language translation of retrieved data U. Amsterdam U. Edinburgh U. Neuchatel Bulg. Ac. of Sciences ITC-Irst U. Limerick U. Helsinki DFKI LIMSI-CNRS

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Discussion on Cross-Language QA CLEF multilingual QA track (like TREC QA) represents a formal evaluation, designed with an eye to replicability. As an exercise, it is an abstraction of the real problems. Future challenges: investigate QA in combination with other applications (for instance summarization) access not only free text, but also different sources of data (multimedia, spoken language, imagery) introduce automated evaluation along with judgments given by humans focus on user’s need: develop real-time interactive systems, which means modeling a potential user and defining suitable answer types.

References: 

References Books Pasca, Marius, Open Domain Question Answering from Large Text Collections, CSLI, 2003. Maybury, Mark (Ed.), New Directions in Question Answering, AAAI Press, 2004. Journals Hirshman, Gaizauskas. Natural Language question answering: the view from here. JNLE, 7 (4), 2001. TREC E. Voorhees. Overview of the TREC 2001 Question Answering Track. M.M. Soubbotin, S.M. Soubbotin. Patterns of Potential Answer Expressions as Clues to the Right Answers. S. Harabagiu, D. Moldovan, M. Pasca, M. Surdeanu, R. Mihalcea, R. Girju, V. Rus, F. Lacatusu, P. Morarescu, R. Brunescu. Answering Complex, List and Context questions with LCC’s Question-Answering Server. C.L.A. Clarke, G.V. Cormack, T.R. Lynam, C.M. Li, G.L. McLearn. Web Reinforced Question Answering (MultiText Experiments for TREC 2001). E. Brill, J. Lin, M. Banko, S. Dumais, A. Ng. Data-Intensive Question Answering.

References: 

References Workshop Proceedings H. Chen and C.-Y. Lin, editors. 2002. Proceedings of the Workshop on Multilingual Summarization and Question Answering at COLING-02, Taipei, Taiwan. M. de Rijke and B. Webber, editors. 2003. Proceedings of the Workshop on Natural Language Processing for Question Answering at EACL-03, Budapest, Hungary. R. Gaizauskas, M. Hepple, and M. Greenwood, editors. 2004. Proceedings of the Workshop on Information Retrieval for Question Answering at SIGIR-04, Sheffield, United Kingdom.

References: 

References N. Kando and H. Ishikawa, editors. 2004. Working Notes of the 4th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and Summarization (NTCIR-04), Tokyo, Japan. M. Maybury, editor. 2003. Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, Stanford, California. C. Peters and F. Borri, editors. 2004. Working Notes of the 5th Cross-Language Evaluation Forum (CLEF-04), Bath, United Kingdom. J. Pustejovsky, editor. 2002. Final Report of the Workshop on TERQAS: Time and Event Recognition in Question Answering Systems, Bedford, Massachusetts. Y. Ravin, J. Prager and S. Harabagiu, editors. 2001. Proceedings of the Workshop on Open-Domain Question Answering at ACL-01, Toulouse, France.

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