Presentation on theme: "RANLP 2005 - Bernardo Magnini1 Open Domain Question Answering: Techniques, Resources and Systems Bernardo Magnini Itc-Irst Trento, Italy"— Presentation transcript:
RANLP 2005 - Bernardo Magnini1 Open Domain Question Answering: Techniques, Resources and Systems Bernardo Magnini Itc-Irst Trento, Italy firstname.lastname@example.org
RANLP 2005 - Bernardo Magnini2 Outline of the Tutorial I. Introduction to QA II. QA at TREC III. System Architecture - Question Processing - Answer Extraction IV. Answer Validation on the Web V. Cross-Language QA
RANLP 2005 - Bernardo Magnini3 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
RANLP 2005 - Bernardo Magnini4 I.Introduction to Question Answering n What is Question Answering n Applications n Users n Question Types n Answer Types n Evaluation n Presentation n Brief history
RANLP 2005 - Bernardo Magnini5 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.
RANLP 2005 - Bernardo Magnini6 Question Answering n Find the answer to a question in a large collection of documents u questions (in place of keyword-based query) u answers (in place of documents)
RANLP 2005 - Bernardo Magnini7 Searching for: Etna Where is Naxos?Searching for: NaxosWhat continent is Taormina in?What is the highest volcano in Europe? Why Question Answering? Document collection From the Caledonian Star in the Mediterranean – September 23, 1990 (www.expeditions.com): On a beautiful early morning the Caledonian Star approaches Naxos, situated on the east coast of Sicily. As we anchored and put the Zodiacs into the sea we enjoyed the great scenery. Under Mount Etna, the highest volcano in Europe, perches the fabulous town of Taormina. This is the goal for our morning. After a short Zodiac ride we embarked our buses with local guides and went up into the hills to reach the town of Taormina. Naxos was the first Greek settlement at Sicily. Soon a harbor was established but the town was later destroyed by invaders.[...] Searching for: Taormina
RANLP 2005 - Bernardo Magnini8 n Document Retrieval u users submit queries corresponding to their information need u system returns (voluminous) list of full-length documents u it is the responsibility of the users to find their original information need, within the returned documents n Open-Domain Question Answering (QA) u users ask fact-based, natural language questions What is the highest volcano in Europe? u system returns list of short answers … Under Mount Etna, the highest volcano in Europe, perches the fabulous town … u more appropriate for specific information needs Alternatives to Information Retrieval
RANLP 2005 - Bernardo Magnini9 What is QA? n Find the answer to a question in a large collection of documents n 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…
RANLP 2005 - Bernardo Magnini10 QA: a Complex Problem (1) n 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 …
RANLP 2005 - Bernardo Magnini11 QA: a Complex Problem (2) n Problem: discovery implicit relations among question and answers Which is the Mozart birth date? …. Mozart (1751 – 1791) ….
RANLP 2005 - Bernardo Magnini12 QA: a complex problem (3) n 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...”.
RANLP 2005 - Bernardo Magnini13 QA: Applications (1) n Information access: u Structured data (databases) u Semi-structured data (e.g. comment field in databases, XML) u Free text n To search over: u The Web u Fixed set of text collection (e.g. TREC) u A single text (reading comprehension evaluation)
RANLP 2005 - Bernardo Magnini14 QA: Applications (2) n Domain independent QA n Domain specific (e.g. help systems) n Multi-modal QA u Annotated images u Speech data
RANLP 2005 - Bernardo Magnini15 QA: Users n Casual users, first time users u Understand the limitations of the system u Interpretation of the answer returned n Expert users u Difference between novel and already provided information u User Model
RANLP 2005 - Bernardo Magnini16 QA: Questions (1) n Classification according to the answer type u Factual questions (What is the larger city …) u Opinions (What is the author attitude …) u Summaries (What are the arguments for and against…) n Classification according to the question speech act: u Yes/NO questions (Is it true that …) u WH questions (Who was the first president …) u Indirect Requests (I would like you to list …) u Commands (Name all the presidents …)
RANLP 2005 - Bernardo Magnini17 QA: Questions (2) n Difficult questions u Why, How questions require understanding causality or instrumental relations u What questions have little constraint on the answer type (e.g. What did they do?)
RANLP 2005 - Bernardo Magnini18 QA: Answers n Long answers, with justification n Short answers (e.g. phrases) n Exact answers (named entities) n Answer construction: u Extraction: cut and paste of snippets from the original document(s) u Generation: from multiple sentences or documents u QA and summarization (e.g. What is this story about?)
RANLP 2005 - Bernardo Magnini19 QA: Information Presentation n Interfaces for QA u Not just isolated questions, but a dialogue u Usability and user satisfaction n Critical situations u Real time, single answer n Dialog-based interaction u Speech input u Conversational access to the Web
RANLP 2005 - Bernardo Magnini20 QA: Brief History (1) n NLP interfaces to databases: u BASEBALL (1961), LUNAR (1973), TEAM (1979), ALFRESCO (1992) u Limitations: structured knowledge and limited domain n Story comprehension: Shank (1977), Kintsch (1998), Hirschman (1999)
RANLP 2005 - Bernardo Magnini21 QA: Brief History (2) n Information retrieval (IR) u Queries are questions u List of documents are answers u QA is close to passage retrieval u Well established methodologies (i.e. Text Retrieval Conferences TREC) n Information extraction (IE): u Pre-defined templates are questions u Filled template are answers
22RANLP 2005 - Bernardo Magnini Research Context (1) Question Answering Domain specificDomain-independent Structured dataFree text Web Fixed set of collections Single document Growing interest in QA (TREC, CLEF, NT evaluation campaign). Recent focus on multilinguality and context aware QA
23RANLP 2005 - Bernardo Magnini 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
RANLP 2005 - Bernardo Magnini24 II.Question Answering at TREC n The problem simplified n Questions and answers n Evaluation metrics n Approaches
RANLP 2005 - Bernardo Magnini25 The problem simplified: The Text Retrieval Conference n Goal u Encourage research in information retrieval based on large-scale collections n Sponsors u NIST: National Institute of Standards and Technology u ARDA: Advanced Research and Development Activity u DARPA: Defense Advanced Research Projects Agency n Since 1999 n Participants are research institutes, universities, industries
RANLP 2005 - Bernardo Magnini26 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
RANLP 2005 - Bernardo Magnini27 Answer Assessment n Criteria for judging an answer u Relevance: it should be responsive to the question u Correctness: it should be factually correct u Conciseness: it should not contain extraneous or irrelevant information u Completeness: it should be complete, i.e. partial answer should not get full credit u Simplicity: it should be simple, so that the questioner can read it easily u Justification: it should be supplied with sufficient context to allow a reader to determine why this was chosen as an answer to the question
RANLP 2005 - Bernardo Magnini28 Questions at TREC
RANLP 2005 - Bernardo Magnini29 Exact Answers n Basic unit of a response: [answer-string, docid] pair n 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
RANLP 2005 - Bernardo Magnini30 Assessments n Four possible judgments for a triple [ Question, document, answer ] n Rigth: the answer is appropriate for the question n Inexact: used for non complete answers n Unsupported: answers without justification n Wrong: the answer is not appropriate for the question
RANLP 2005 - Bernardo Magnini31 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?
RANLP 2005 - Bernardo Magnini32 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.
RANLP 2005 - Bernardo Magnini33 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).
RANLP 2005 - Bernardo Magnini34 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.
RANLP 2005 - Bernardo Magnini35 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.
RANLP 2005 - Bernardo Magnini36 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.
RANLP 2005 - Bernardo Magnini37 TREC Evaluation Metric: Mean Reciprocal Rank (MRR) n Reciprocal Rank = inverse of rank at which first correct answer was found: [1, 0,5, 0.33, 0.25, 0.2, 0] n MRR: average over all questions n Strict score: unsupported count as incorrect n Lenient score: unsupported count as correct
RANLP 2005 - Bernardo Magnini38 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
RANLP 2005 - Bernardo Magnini39 Evaluation n Best result: 67% n Average over 67 runs:23% TREC-8 TREC-9TREC-10 66% 25% 58% 24% 67% 23%
RANLP 2005 - Bernardo Magnini40 Main Approaches at TREC n Knowledge-Based n Web-based n Pattern-based
RANLP 2005 - Bernardo Magnini41 Knowledge-Based Approach n Linguistic-oriented methodology u Determine the answer type from question formanswer type u Retrieve small portions of documents u Find entities matching the answer type category in text snippets n Majority of systems use a lexicon (usually WordNet) u To find answer type u To verify that a candidate answer is of the correct typecandidate answer u To get definitions n Complex architecture...
RANLP 2005 - Bernardo Magnini42 Web-Based Approach QUESTION Question Processing Component Search Component Auxiliary Corpus WEB ANSWER TREC Corpus Answer Extraction Component
RANLP 2005 - Bernardo Magnini43 Patter-Based Approach (1/3) n Knowledge poor Knowledge poor n Strategy u Search for predefined patterns of textual expressions that may be interpreted as answers to certain question types. u The presence of such patterns in answer string candidates may provide evidence of the right answer.patterns
RANLP 2005 - Bernardo Magnini44 Patter-Based Approach (2/3) n Conditions u Detailed categorization of question types F Up to 9 types of the “Who” question; 35 categories in total u Significant number of patterns corresponding to each question type F Up to 23 patterns for the “Who-Author” type, average of 15 u Find multiple candidate snippets and check for the presence of patterns (emphasis on recall)
RANLP 2005 - Bernardo Magnini46 Use of answer patterns 1.For generating queries to the search engine. How did Mahatma Gandhi die? Mahatma Gandhi die Mahatma Gandhi die of Mahatma Gandhi lost his life in 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. 2.For answer extraction When was Mozart born? P=1 ( - DATE) P=.69 was born on
RANLP 2005 - Bernardo Magnini47 Acquisition of Answer Patterns Relevant approaches: u Manually developed surface pattern library (Soubbotin, Soubbotin, 2001) u Automatically extracted surface patterns (Ravichandran, Hovy 2002) Patter learning: 1. Start with a seed, e.g. (Mozart, 1756) 2. Download Web documents using a search engine 3. Retain sentences that contain both question and answer terms 4. Construct a suffix tree for extracting the longest matching substring that spans and 5. Calculate precision of patterns Precision = # of correct patterns with correct answer / # of total patterns
RANLP 2005 - Bernardo Magnini48 Capturing variability with patterns n Pattern based QA is more effective when supported by variable typing obtained using NLP techniques and resources. When was born? ( - was born in n Surface patterns can not deal with word reordering and apposition phrases: Galileo, the famous astronomer, was born in … n 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
RANLP 2005 - Bernardo Magnini49 Syntactic answer patterns (1) S NP The VP was inventedPP in Answer patterns that capture the syntactic relations of a sentence. When was invented?
RANLP 2005 - Bernardo Magnini50 Syntactic answer patterns (2) S NP VP The first was inventedPP in 1877 phonograph The matching phase turns out to be a problem of partial match among syntactic trees.
RANLP 2005 - Bernardo Magnini51 III. System Architecture n Knowledge Based approach u Question Processing u Search component u Answer Extraction
RANLP 2005 - Bernardo Magnini52 Knowledge based QA Search Component ANSWER Answer Extraction Component ANSWER VALIDATION NAMED ENTITIES RECOGNITION PARAGRAPH FILTERING ANSWER IDENTIFICATION QUERY COMPOSITION SEARCH ENGINE Document collection MULTIWORDS RECOGNITION KEYWORDS EXPANSION WORD SENSE DISAMBIGUATION QUESTION PARSING ANSWER TYPE IDENTIFICATION TOKENIZATION & POS TAGGING QUESTION Question Processing Component
RANLP 2005 - Bernardo Magnini53 Question Analysis (1) n Input: NLP question n Output: u query for the search engine (i.e. a boolean composition of weighted keywords) u Answer type u Additional constraints: question focus, syntactic or semantic relations that should hold for a candidate answer entity and other entities
RANLP 2005 - Bernardo Magnini54 Question Analysis (2) n Steps: 1. Tokenization 2. POS-tagging 3. Multi-words recognition 4. Parsing 5. Answer type and focus identification 6. Keyword extraction 7. Word Sense Disambiguation 8. Expansions
RANLP 2005 - Bernardo Magnini55 Tokenization and POS-tagging NL-QUESTION: Who was the inventor of the electric light? WhoWhoCCHI[0,0] wasbeVIY[1,1] thedetRS[2,2] inventorinventorSS[3,3] ofofES[4,4] thedetRS[5,5] electricelectricAS[6,6] lightlightSS[7,7] ??XPS[8,8]
RANLP 2005 - Bernardo Magnini56 Multi-Words recognition NL-QUESTION: Who was the inventor of the electric light? WhoWhoCCHI[0,0] wasbeVIY[1,1] thedetRS[2,2] inventorinventorSS[3,3] ofofES[4,4] thedetRS[5,5] electric_light electric_light SS[6,7] ??XPS[8,8]
RANLP 2005 - Bernardo Magnini57 Syntactic Parsing n Identify syntactic structure of a sentence u 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?
RANLP 2005 - Bernardo Magnini58 Answer Type and Focus n Focus is the word that expresses the relevant entity in the question u Used to select a set of relevant documents u ES: Where was Mozart born? n Answer Type is the category of the entity to be searched as answer u PERSON, MEASURE, TIME PERIOD, DATE, ORGANIZATION, DEFINITION u ES: Where was Mozart born? F LOCATION
RANLP 2005 - Bernardo Magnini59 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)
RANLP 2005 - Bernardo Magnini60 Keywords Extraction NL-QUESTION: Who was the inventor of the electric light? WhoWhoCCHI[0,0] wasbeVIY[1,1] thedetRS[2,2] inventorinventorSS[3,3] ofofES[4,4] thedetRS[5,5] electric_light electric_light SS[6,7] ??XPS[8,8]
RANLP 2005 - Bernardo Magnini61 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 VISIBLEvisible#1: conspicuous obvious PHYSICS visible#2: visible seeable ASTRONOMY EARTHearth#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
RANLP 2005 - Bernardo Magnini62 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
RANLP 2005 - Bernardo Magnini63 Keyword Composition n Keywords and expansions are composed in a boolean expression with AND/OR operators n Several possibilities: u AND composition u Cartesian composition (OR (inventor AND electric_light) OR (inventor AND incandescent_lamp) OR (discoverer AND electric_light) ………………………… OR inventor OR electric_light))
RANLP 2005 - Bernardo Magnini64 Document Collection Pre-processing n For real time QA applications off-line pre-processing of the text is necessary u Term indexing u POS-tagging u Named Entities Recognition
RANLP 2005 - Bernardo Magnini65 Candidate Answer Document Selection n Passage Selection: Individuate relevant, small, text portions n Given a document and a list of keywords: u Paragraph length (e.g. 200 words) u Consider the percentage of keywords present in the passage u Consider if some keyword is obligatory (e.g. the focus of the question).
RANLP 2005 - Bernardo Magnini66 Candidate Answer Document Analysis n Passage text tagging n Named Entity Recognition Who is the author of the “Star Spangled Banner”? … Francis Scott Key wrote the “Star Spangled Banner” in 1814 n Some systems: u passages parsing (Harabagiu, 2001) u Logical form (Zajac, 2001)
RANLP 2005 - Bernardo Magnini67 Answer Extraction (1) n Who is the author of the “Star Spangled Banner”? … Francis Scott Key wrote the “Star Spangled Banner” in 1814 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
RANLP 2005 - Bernardo Magnini68 Answer Identification Thomas E. Edison
RANLP 2005 - Bernardo Magnini69 IV. Answer Validation n Automatic answer validation n Approach: u web-based u use of patterns u combine statistics and linguistic information n Discussion n Conclusions
RANLP 2005 - Bernardo Magnini71 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
RANLP 2005 - Bernardo Magnini72 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
RANLP 2005 - Bernardo Magnini73 Requirements for Automatic AV n Accuracy: it has to compare well with respect to human judgments n Efficiency: large scale (Web), real time scenarios n Simplicity: avoid the complexity of QA systems
RANLP 2005 - Bernardo Magnini74 Approach n Web-based u take advantage of Web redundancy n Pattern-based u the Web is mined using patterns (i.e. validation patterns) extracted from the question and the candidate answer n Quantitative (as opposed to content-based) u 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)
RANLP 2005 - Bernardo Magnini75 Web Redundancy What is the capital of the USA? Capital Region USA: Fly-Drive Holidays in and Around Washington D.C. the Insider’s Guide to the Capital Area Music Scene (Washington D.C., USA). The Capital Tangueros (Washington DC Area, USA) I live in the Nations’s Capital, Washington Metropolitan Area (USA) In 1790 Capital (also USA’s capital): Washington D.C. Area: 179 square km Washington
RANLP 2005 - Bernardo Magnini76 Validation Pattern Capital Region USA: Fly-Drive Holidays in and Around Washington D.C. the Insider’s Guide to the Capital Area Music Scene (Washington D.C., USA). The Capital Tangueros (Washington DC Area, USA) I live in the Nations’s Capital, Washington Metropolitan Area (USA) In 1790 Capital (also USA’s capital): Washington D.C. Area: 179 square km [Capital NEAR USA NEAR Washington]
RANLP 2005 - Bernardo Magnini77 Related Work n Pattern-based QA u Brill, 2001 – TREC-10 u Subboting, 2001 – TREC-10 u Ravichandran and Hovy, ACL-02 n Use of the Web for QA u Clarke et al. 2001 – TREC-10 u Radev, et al. 2001 - CIKM n Statistical approach on the Web u PMI-IR: Turney, 2001 and ACL-02
RANLP 2005 - Bernardo Magnini78 Architecture questioncandidate answer validation pattern answer validity score correct answer wrong answer > t < t #doc filtering #doc < k
RANLP 2005 - Bernardo Magnini79 Architecture questioncandidate answer validation pattern answer validity score correct answer wrong answer > t < t #doc filtering #doc < k
RANLP 2005 - Bernardo Magnini80 Extracting Validation Patterns questioncandidate answer question pattern (Qp) answer pattern (Ap) stop-word filter term expansion validation pattern answer type named entity recognition stop-word filter
RANLP 2005 - Bernardo Magnini81 Architecture questioncandidate answer validation pattern answer validity score correct answer wrong answer > t < t #doc filtering #doc < k
RANLP 2005 - Bernardo Magnini82 Answer Validity Score n PMI-IR algorithm (Turney, 2001) PMI (Qp, Ap) = P(Qp, Ap) P(Qp) * P(Ap) n The result is interpreted as evidence that the validation pattern is consistent, which imply answer accuracy
RANLP 2005 - Bernardo Magnini83 Answer Validity Score PMI (Qp, Ap) = hits(Qp NEAR Ap) hits(Qp) * hits(Ap) n Three searches are submitted to the Web: hits(Qp) hits(Ap) hits(Qp NEAR Ap)
RANLP 2005 - Bernardo Magnini84 Example What county is Modesto, California in? Answer type: Location Qp = [county NEAR Modesto NEAR California] P(Qp) = P(county, Modesto, California) = 909 3 *10 8 A1= The Stanislaus County district attorney’s A2 = In Modesto, San Francisco, and Stop-word filter
RANLP 2005 - Bernardo Magnini85 Example (cont.) The Stanislaus County district attorney’s A1p = [ Stanislaus] In Modesto, San Francisco, and A2p = [ San Francisco] P(Stanislaus) = 73641 3 *10 8 P(San Francisco)= 4072519 3 *10 8 NER(location)
RANLP 2005 - Bernardo Magnini86 Example (cont.) PMI(Qp, A1p) = 2473 P(Qp, A1p) = 552 3 *10 8 P(Qp, A2p) = 11 3 *10 8 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
RANLP 2005 - Bernardo Magnini87 Experiments n Data set: u 492 TREC-2001 questions u 2726 answers: 3 correct answers and 3 wrong answers for each question, randomly selected from TREC-10 participants human-judged corpus n Search engine: Altavista u allows the NEAR operator
RANLP 2005 - Bernardo Magnini88 Experiment: Answers Q-916: What river in the US is known as the Big Muddy ? The Mississippi Known as Big Muddy, the Mississippi is the longest as Big Muddy, the Mississippi is the longest messed with. Known as Big Muddy, the Mississip Mississippi is the longest river in the US the Mississippi is the longest river(Mississippi) has brought the Mississippi to its lowest ipes.In Life on the Mississippi,Mark Twain wrote t Southeast;Mississippi;Mark Twain; officials began Known; Mississippi; US; Minnesota; Gulf Mexico Mud Island,;Mississippi;”The;--history,;Memphis
RANLP 2005 - Bernardo Magnini89 Baseline n Consider the documents provided by NIST to TREC-10 participants (1000 documents for each question) n 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
RANLP 2005 - Bernardo Magnini90 Asymmetrical Measures n Problem: some candidate answers (e.g. numbers) produce an enormous amount of Web documents n Scores for good (Ac) and bad (Aw) answers tend to be similar, making the choice more difficult ~ PMI(q, a c ) = PMI (q, a w ) How many Great Lakes are there? … to cross all five Great Lakes completed a 19.2 …
RANLP 2005 - Bernardo Magnini92 Comparing PMI and ACP ACP increases the difference between the right and the wrong answer.
RANLP 2005 - Bernardo Magnini93 Results BaselineMLHRPMIACP Absolute threshold 52.977.477.778.4 Relative threshold 52.979.679.581.2 SR on all 492 TREC-2001 questions BaselineMLHRPMIACP Absolute threshold 82.183.3 Relative threshold 84.484.986.3 SR on all 249 factoid questions
RANLP 2005 - Bernardo Magnini94 Discussion (1) n Definition questions are the more problematic u on the subset of 249 named-entities questions success rate is higher (i.e. 86.3) n Relative threshold improve performance (+ 2%) over fixed threshold n Non symmetric measures of co-occurrence work better for answer validation (+ 2%) n Source of errors: u Answer type recognition u Named-entities recognition u TREC answer set (e.g. tokenization)
RANLP 2005 - Bernardo Magnini95 Discussion (2) n Automatic answer validation is a key challenge for Web-based question/answering systems n Requirements: u accuracy with respect to human judgments: 80% success rate is a good starting point u efficiency: documents are not downloaded u simplicity: based on patterns n At present, it is suitable for a generate&test component integrated in a QA system
RANLP 2005 - Bernardo Magnini96 V. Cross-Language QA n Motivations n QA@CLEF QA@CLEF n Performances n Approaches
RANLP 2005 - Bernardo Magnini97 Motivations n Answers may be found in languages different from the language of the question. n Interest in QA systems for languages other than English. n Force the QA community to design real multilingual systems. n Check/improve the portability of the technologies implemented in current English QA systems.
RANLP 2005 - Bernardo Magnini98 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 corpusItalian corpusSpanish corpusFrench corpus
RANLP 2005 - Bernardo Magnini99 CL-QA at CLEF n Adopt the same rules used at TREC QA u Factoid questions (i.e. no definition questions) u Exact answers + document id n Use the CLEF corpora (news, 1994 -1995) n Return the answer in the language of the text collection in which it has been found (i.e. no translation of the answer) n QA-CLEF-2003 was an initial step toward a more complex task organized at CLEF-2004 and 2005.
100RANLP 2005 - Bernardo Magnini 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)
101RANLP 2005 - Bernardo Magnini CLEF QA - Overview document collections translation EN => 7 languages systems’ answers 100 monolingual Q&A pairs with EN translation IT FR NL ES … 700 Q&A pairs in 1 language + EN selection of additional 80 + 20 questions Multieight-04 XML collection of 700 Q&A in 8 languages extraction of plain text test sets experiments (1 week window) manual assessment question generation (2.5 p/m per group) Exercise (10-23/5) evaluation (2 p/d for 1 run)
102RANLP 2005 - Bernardo Magnini 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). DEENESFRITNLPT BG DE EN ES FI FR IT NL PT S T 6 monolingual and 50 bilingual tasks. Teams participated in 19 tasks,
103RANLP 2005 - Bernardo Magnini 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
104RANLP 2005 - Bernardo Magnini CLEF QA – Multieight Как умира Пазолини? TRANSLATION[убит] Auf welche Art starb Pasolini? TRANSLATION[ermordet] ermordet How did Pasolini die? TRANSLATION[murdered] murdered ¿Cómo murió Pasolini? TRANSLATION[Asesinado] Brutalmente asesinado en los arrabales de Ostia Comment est mort Pasolini ? TRANSLATION[assassiné] assassiné assassiné en novembre 1975 dans des circonstances mystérieuses assassiné il y a 20 ans Come è morto Pasolini? TRANSLATION[assassinato] massacrato e abbandonato sulla spiaggia di Ostia Hoe stierf Pasolini? TRANSLATION[vermoord] vermoord Como morreu Pasolini? assassinado
105RANLP 2005 - Bernardo Magnini 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: 1 Q Q i=1 number of correct responses in first i ranks i CWS =
106RANLP 2005 - Bernardo Magnini Evaluation Exercise - Participants AmericaEuropeAsiaAustraliaTOTAL submitted runs TREC-8133312046 TREC-91476-2775 TREC-101988-3567 TREC-1116106-3267 TREC-121384-2554 NTCIR-3 (QAC-1)1-15-1636 CLEF 200335--817 CLEF 2004117--1848 Distribution of participating groups in different QA evaluation campaigns.
107RANLP 2005 - Bernardo Magnini Evaluation Exercise - Participants Number of participating teams-number of submitted runs at CLEF 2004. Comparability issue. DEENESFRITNLPT BG 1-11-2 DE 2-22-2 2-31-2 EN 1-21-1 ES 5-81-2 FI 1-1 FR 3-61-2 IT 1-2 2-3 NL 1-2 PT 1-22-3 ST
108RANLP 2005 - Bernardo Magnini 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 83 22 70 21.4 41.5 29 35 17 45.5 23.7 35 14.7 accuracy (%) TREC-8 TREC-9TREC-10TREC-11TREC-12 * CLEF-2003 ** monol. bil. CLEF-2004 monol. bil. best system average
109RANLP 2005 - Bernardo Magnini 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
110RANLP 2005 - Bernardo Magnini 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.
RANLP 2005 - Bernardo Magnini111 References n Books n Pasca, Marius, Open Domain Question Answering from Large Text Collections, CSLI, 2003. n Maybury, Mark (Ed.), New Directions in Question Answering, AAAI Press, 2004. n Journals n Hirshman, Gaizauskas. Natural Language question answering: the view from here. JNLE, 7 (4), 2001. n TREC n E. Voorhees. Overview of the TREC 2001 Question Answering Track. n M.M. Soubbotin, S.M. Soubbotin. Patterns of Potential Answer Expressions as Clues to the Right Answers. n 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. n 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). n E. Brill, J. Lin, M. Banko, S. Dumais, A. Ng. Data-Intensive Question Answering.
RANLP 2005 - Bernardo Magnini112 References n Workshop Proceedings n H. Chen and C.-Y. Lin, editors. 2002. Proceedings of the Workshop on Multilingual Summarization and Question Answering at COLING-02, Taipei, Taiwan. n M. de Rijke and B. Webber, editors. 2003. Proceedings of the Workshop on Natural Language Processing for Question Answering at EACL-03, Budapest, Hungary. n 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.
RANLP 2005 - Bernardo Magnini113 References n 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. n M. Maybury, editor. 2003. Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, Stanford, California. n C. Peters and F. Borri, editors. 2004. Working Notes of the 5th Cross- Language Evaluation Forum (CLEF-04), Bath, United Kingdom. n J. Pustejovsky, editor. 2002. Final Report of the Workshop on TERQAS: Time and Event Recognition in Question Answering Systems, Bedford, Massachusetts. n Y. Ravin, J. Prager and S. Harabagiu, editors. 2001. Proceedings of the Workshop on Open-Domain Question Answering at ACL-01, Toulouse, France.