What is Ques on Answering? Dan Jurafsky Ques on Answering One of the oldest NLP tasks (punched card systems in 1961) Simmons, Klein, McConlogue. 1964. Indexing and : : Dependency Logic for Answering English Ques ons. Ques%on Poten%al-Answers American Documenta on 15:30, 196-204 What do worms eat? Worms eat grass Horses with worms eat grass worms horses worms eat with eat eat grass worms grass what Birds eat worms Grass is eaten by worms birds worms
eat eat
2 worms grass Dan Jurafsky
Ques on Answering: IBM’s Watson • Won Jeopardy on February 16, 2011!
WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” Bram Stoker INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL
3 Dan Jurafsky
Apple’s Siri
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Wolfram Alpha
5 Dan Jurafsky Types of Ques ons in Modern Systems
• Factoid ques ons • Who wrote “The Universal Declara on of Human Rights”? • How many calories are there in two slices of apple pie? • What is the average age of the onset of au sm? • Where is Apple Computer based? • Complex (narra ve) ques ons: • In children with an acute febrile illness, what is the efficacy of acetaminophen in reducing fever? • What do scholars think about Jefferson’s posi on on dealing with pirates? 6 Dan Jurafsky Commercial systems: mainly factoid ques ons
Where is the Louvre Museum located? In Paris, France What’s the abbrevia on for limited L.P. partnership? What are the names of Odin’s ravens? Huginn and Muninn What currency is used in China? The yuan What kind of nuts are used in marzipan? almonds What instrument does Max Roach play? drums What is the telephone number for Stanford 650-723-2300 University? Dan Jurafsky
Paradigms for QA • IR-based approaches • TREC; IBM Watson; Google • Knowledge-based and Hybrid approaches • IBM Watson; Apple Siri; Wolfram Alpha; True Knowledge Evi
8 Dan Jurafsky Many ques ons can already be answered by web search
• a
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IR-based Ques on Answering
• a
10 Dan Jurafsky IR-based Factoid QA
DocumentDocument Document Document Document Document Indexing Answer
Passage Question Retrieval Processing Docume Document Query Document Document Passage Answer Document DocumeRelevantnt passages Formulation Retrieval nt Retrieval Processing Question Docs Answer Type Detection
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IR-based Factoid QA • QUESTION PROCESSING • Detect ques on type, answer type, focus, rela ons • Formulate queries to send to a search engine • PASSAGE RETRIEVAL • Retrieve ranked documents • Break into suitable passages and rerank • ANSWER PROCESSING • Extract candidate answers • Rank candidates • using evidence from the text and external sources Dan Jurafsky
Knowledge-based approaches (Siri)
• Build a seman c representa on of the query • Times, dates, loca ons, en es, numeric quan es • Map from this seman cs to query structured data or resources • Geospa al databases • Ontologies (Wikipedia infoboxes, dbPedia, WordNet, Yago) • Restaurant review sources and reserva on services • Scien fic databases
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Hybrid approaches (IBM Watson)
• Build a shallow seman c representa on of the query • Generate answer candidates using IR methods • Augmented with ontologies and semi-structured data • Score each candidate using richer knowledge sources • Geospa al databases • Temporal reasoning • Taxonomical classifica on
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What is Ques on Answering? Question Answering
Answer Types and Query Formula on Dan Jurafsky Factoid Q/A
DocumentDocument Document Document Document Document Indexing Answer
Passage Question Retrieval Processing Docume Document Query Document Document Passage Answer Document DocumeRelevantnt passages Formulation Retrieval nt Retrieval Processing Question Docs Answer Type Detection
17 Dan Jurafsky Ques on Processing Things to extract from the ques on
• Answer Type Detec on • Decide the named en ty type (person, place) of the answer • Query Formula on • Choose query keywords for the IR system • Ques on Type classifica on • Is this a defini on ques on, a math ques on, a list ques on? • Focus Detec on • Find the ques on words that are replaced by the answer • Rela on Extrac on 18 • Find rela ons between en es in the ques on Dan Jurafsky Question Processing
They’re the two states you could be reentering if you’re crossing Florida’s northern border
• Answer Type: US state • Query: two states, border, Florida, north • Focus: the two states • Rela ons: borders(Florida, ?x, north)
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Answer Type Detec on: Named En es • Who founded Virgin Airlines? • PERSON • What Canadian city has the largest popula on? • CITY. Dan Jurafsky Answer Type Taxonomy Xin Li, Dan Roth. 2002. Learning Ques on Classifiers. COLING'02 • 6 coarse classes • ABBEVIATION, ENTITY, DESCRIPTION, HUMAN, LOCATION, NUMERIC • 50 finer classes • LOCATION: city, country, mountain… • HUMAN: group, individual, tle, descrip on • ENTITY: animal, body, color, currency…
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Part of Li & Roth’s Answer Type Taxonomy
country city state
reason expression LOCATION definition abbreviation ABBREVIATION DESCRIPTION individual food ENTITY HUMAN title currency NUMERIC group
animal date money percent
distance size 22 Dan Jurafsky Answer Types
23 Dan Jurafsky More Answer Types
24 Dan Jurafsky Answer types in Jeopardy
Ferrucci et al. 2010. Building Watson: An Overview of the DeepQA Project. AI Magazine. Fall 2010. 59-79.
• 2500 answer types in 20,000 Jeopardy ques on sample • The most frequent 200 answer types cover < 50% of data • The 40 most frequent Jeopardy answer types he, country, city, man, film, state, she, author, group, here, company, president, capital, star, novel, character, woman, river, island, king, song, part, series, sport, singer, actor, play, team, show, actress, animal, presiden al, composer, musical, na on, book, tle, leader, game
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Answer Type Detec on
• Hand-wri en rules • Machine Learning • Hybrids Dan Jurafsky
Answer Type Detec on
• Regular expression-based rules can get some cases: • Who {is|was|are|were} PERSON • PERSON (YEAR – YEAR) • Other rules use the ques on headword: (the headword of the first noun phrase a er the wh-word)
• Which city in China has the largest number of foreign financial companies? • What is the state flower of California? Dan Jurafsky
Answer Type Detec on • Most o en, we treat the problem as machine learning classifica on • Define a taxonomy of ques on types • Annotate training data for each ques on type • Train classifiers for each ques on class using a rich set of features. • features include those hand-wri en rules!
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Features for Answer Type Detec on
• Ques on words and phrases • Part-of-speech tags • Parse features (headwords) • Named En es • Seman cally related words
29 Dan Jurafsky Factoid Q/A
DocumentDocument Document Document Document Document Indexing Answer
Passage Question Retrieval Processing Docume Document Query Document Document Passage Answer Document DocumeRelevantnt passages Formulation Retrieval nt Retrieval Processing Question Docs Answer Type Detection
30 Dan Jurafsky Keyword Selec on Algorithm Dan Moldovan, Sanda Harabagiu, Marius Paca, Rada Mihalcea, Richard Goodrum, Roxana Girju and Vasile Rus. 1999. Proceedings of TREC-8. 1. Select all non-stop words in quota ons 2. Select all NNP words in recognized named en es 3. Select all complex nominals with their adjec val modifiers 4. Select all other complex nominals 5. Select all nouns with their adjec val modifiers 6. Select all other nouns 7. Select all verbs 8. Select all adverbs 9. Select the QFW word (skipped in all previous steps) 10. Select all other words Dan Jurafsky Choosing keywords from the query Slide from Mihai Surdeanu Who coined the term “cyberspace” in his novel “Neuromancer”?
1 1
4 4
7
cyberspace/1 Neuromancer/1 term/4 novel/4 coined/7 32 Question Answering
Answer Types and Query Formula on Question Answering
Passage Retrieval and Answer Extrac on Dan Jurafsky Factoid Q/A
DocumentDocument Document Document Document Document Indexing Answer
Passage Question Retrieval Processing Docume Document Query Document Document Passage Answer Document DocumeRelevantnt passages Formulation Retrieval nt Retrieval Processing Question Docs Answer Type Detection
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Passage Retrieval
• Step 1: IR engine retrieves documents using query terms • Step 2: Segment the documents into shorter units • something like paragraphs • Step 3: Passage ranking • Use answer type to help rerank passages
36 Dan Jurafsky Features for Passage Ranking
Either in rule-based classifiers or with supervised machine learning
• Number of Named En es of the right type in passage • Number of query words in passage • Number of ques on N-grams also in passage • Proximity of query keywords to each other in passage • Longest sequence of ques on words • Rank of the document containing passage Dan Jurafsky Factoid Q/A
DocumentDocument Document Document Document Document Indexing Answer
Passage Question Retrieval Processing Docume Document Query Document Document Passage Answer Document DocumeRelevantnt passages Formulation Retrieval nt Retrieval Processing Question Docs Answer Type Detection
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Answer Extrac on
• Run an answer-type named-en ty tagger on the passages • Each answer type requires a named-en ty tagger that detects it • If answer type is CITY, tagger has to tag CITY • Can be full NER, simple regular expressions, or hybrid • Return the string with the right type: • Who is the prime minister of India (PERSON) Manmohan Singh, Prime Minister of India, had told left leaders that the deal would not be renegotiated.! • How tall is Mt. Everest? (LENGTH) The official height of Mount Everest is 29035 feet! Dan Jurafsky Ranking Candidate Answers
• But what if there are mul ple candidate answers!
Q: Who was Queen Victoria’s second son?! • Answer Type: Person • Passage: The Marie biscuit is named a er Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert Dan Jurafsky Ranking Candidate Answers
• But what if there are mul ple candidate answers!
Q: Who was Queen Victoria’s second son?! • Answer Type: Person • Passage: The Marie biscuit is named a er Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert Dan Jurafsky Use machine learning: Features for ranking candidate answers
Answer type match: Candidate contains a phrase with the correct answer type. Pa ern match: Regular expression pa ern matches the candidate. Ques on keywords: # of ques on keywords in the candidate. Keyword distance: Distance in words between the candidate and query keywords Novelty factor: A word in the candidate is not in the query. Apposi on features: The candidate is an apposi ve to ques on terms Punctua on loca on: The candidate is immediately followed by a comma, period, quota on marks, semicolon, or exclama on mark. Sequences of ques on terms: The length of the longest sequence of ques on terms that occurs in the candidate answer.
Dan Jurafsky
Candidate Answer scoring in IBM Watson
• Each candidate answer gets scores from >50 components • (from unstructured text, semi-structured text, triple stores)
• logical form (parse) match between ques on and candidate • passage source reliability • geospa al loca on • California is ”southwest of Montana” • temporal rela onships
43 • taxonomic classifica on Dan Jurafsky Common Evalua on Metrics
1. Accuracy (does answer match gold-labeled answer?) 2. Mean Reciprocal Rank • For each query return a ranked list of M candidate answers. • Query score is 1/Rank of the first correct answer • If first answer is correct: 1 N 1 • else if second answer is correct: ½ ∑ rank • else if third answer is correct: ⅓, etc. MRR = i=1 i • Score is 0 if none of the M answers are correct N • Take the mean over all N queries 44 Question Answering
Passage Retrieval and Answer Extrac on Question Answering
Using Knowledge in QA Dan Jurafsky
Rela on Extrac on • Answers: Databases of Rela ons • born-in(“Emma Goldman”, “June 27 1869”) • author-of(“Cao Xue Qin”, “Dream of the Red Chamber”) • Draw from Wikipedia infoboxes, DBpedia, FreeBase, etc. • Ques ons: Extrac ng Rela ons in Ques ons Whose granddaughter starred in E.T.? (acted-in ?x “E.T.”)!
47 (granddaughter-of ?x ?y)! Dan Jurafsky
Temporal Reasoning • Rela on databases • (and obituaries, biographical dic onaries, etc.) • IBM Watson ”In 1594 he took a job as a tax collector in Andalusia” Candidates: • Thoreau is a bad answer (born in 1817) • Cervantes is possible (was alive in 1594)
48 Dan Jurafsky Geospa al knowledge (containment, direc onality, borders) • Beijing is a good answer for ”Asian city” • California is ”southwest of Montana” • geonames.org:
49 Dan Jurafsky Context and Conversa on in Virtual Assistants like Siri
• Coreference helps resolve ambigui es U: “Book a table at Il Fornaio at 7:00 with my mom” U: “Also send her an email reminder” • Clarifica on ques ons: U: “Chicago pizza” S: “Did you mean pizza restaurants in Chicago or Chicago-style pizza?”
50 Question Answering
Using Knowledge in QA Question Answering
Ques on Answering in Watson (Deep QA) Dan Jurafsky
Ques on Answering: IBM’s Watson • Won Jeopardy on February 16, 2011!
WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” Bram Stoker INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL
53 Dan Jurafsky The Architecture of Watson
(2) Candidate Answer Generation Question (3) Candidate (4) Answer Confidence From Text Resources Scoring Candidate Merging Document Answer and Candidate and Answer Candidate Ranking Passsage Answer Answer (1) Question passages Extraction Retrieval Candidate + Processing Document titles Candidate Confidence Merge CandidateAnswer Anchor text Answer Evidence Candidate Equivalent Focus Detection Answer DocumentDocument Retrieval Answer Document Document Candidate Answers and scoring + Lexical Document Document Answer Candidate Confidence Answer Type Answer Detection Candidate Candidate Candidate Answer Text Time from Answer Logistic Answer Question From Structured Data Evidence DBPedia + Regression Classification Candidate Sources Answer Answer ConfidenceCandidate Answer Ranker Text Type Answer Relation Candidate Parsing Evidence + Retrieval Answer Sources Candidate Candidate Confidence Named Entity Answer Answer Space from Tagging Facebook + Answer DBPedia Freebase Confidence and Relation Extraction Confidence Coreference Dan Jurafsky
Stage 1: Ques on Processing
• Parsing • Named En ty Tagging • Rela on Extrac on • Focus • Answer Type • Ques on Classifica on
55 Named En ty and Parse Focus Answer Type Rela on Extrac on GEO Poets and Poetry: He was a bank clerk in the Yukon before he published “Songs of a Sourdough” in 1907.
COMPOSITION YEAR PERSON THEATRE: A new play based on this Sir Arthur Conan Doyle canine classic opened on the London stage in 2007. GEO YEAR authorof(focus,“Songs of a sourdough”) publish (e1, he, “Songs of a sourdough”) in (e2, e1, 1907) temporallink(publish(...), 1907) Dan Jurafsky
Focus extrac on • Focus: the part of the ques on that co-refers with the answer • Replace it with answer to find a suppor ng passage. • Extracted by hand-wri en rules • "Extract any noun phrase with determiner this”
57 • “Extrac ng pronouns she, he, hers, him, “! Dan Jurafsky
Lexical Answer Type
country city state
reason expression LOCATION definition abbreviation • The seman c class of the answer ABBREVIATION DESCRIPTION individual food ENTITY HUMAN title • But for Jeopardy the TREC answer currency NUMERIC group
animal date money percent type taxonomy is insufficient distance size • DeepQA team inves gated 20,000 ques ons • 100 named en es only covered <50% of the ques ons! • Instead: Extract lots of words: 5,000 for those 20,000
58 ques ons Dan Jurafsky
Lexical Answer Type • Answer types extracted by hand-wri en rules • Syntac c headword of the focus. • Words that are coreferent with the focus • Jeopardy! category, if refers to compa ble en ty.
Poets and Poetry: He was a bank clerk in the Yukon before he published “Songs of a Sourdough” in 1907. 59 Dan Jurafsky
Rela on Extrac on in DeepQA
• For the most frequent 30 rela ons: • Hand-wri en regular expressions • AuthorOf: • Many pa erns such as one to deal with: • a Mary Shelley tale, the Saroyan novel, Twain’s travel books, a1984 Tom Clancy thriller • [Author] [Prose] • For the rest: distant supervision
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Stage 2: Candidate Answer Genera on
61 Dan Jurafsky Extrac ng candidate answers from triple stores • If we extracted a rela on from the ques on … he published “Songs of a sourdough” (author-of ?x “Songs of a sourdough”)! • We just query a triple store • Wikipedia infoboxes, DBpedia, FreeBase, etc. • born-in(“Emma Goldman”, “June 27 1869”) • author-of(“Cao Xue Qin”, “Dream of the Red Chamber”)
62 • author-of(“Songs of a sourdough”, “Robert Service”)
! Dan Jurafsky Extrac ng candidate answers from text: get documents/passages 1. Do standard IR-based QA to get documents Robert Redford and Paul Newman starred in this depression-era gri er flick. (2.0 Robert Redford) (2.0 Paul Newman) star depression era gri er (1.5 flick)
63 ! Dan Jurafsky Extrac ng answers from documents/ passages • Useful fact: Jeopardy! answers are mostly the tle of a Wikipedia document • If the document is a Wikipedia ar cle, just take the tle • If not, extract all noun phrases in the passage that are Wikipedia document tles • Or extract all anchor text The Sting! 64
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Stage 3: Candidate Answer Scoring
• Use lots of sources of evidence to score an answer • more than 50 scorers • Lexical answer type is a big one • Different in DeepQA than in pure IR factoid QA • In pure IR factoid QA, answer type is used to strictly filter answers • In DeepQA, answer type is just one of many pieces of evidence 65 Dan Jurafsky Lexical Answer Type (LAT) for Scoring Candidates
• Given: • candidate answer & lexical answer type • Return a score: can answer can be a subclass of this answer type? • Candidate: “difficulty swallowing” & LAT “condi on” 1. Check DBPedia, WordNet, etc • difficulty swallowing -> Dbpedia Dysphagia -> WordNet Dysphagia • condi on-> WordNet Condi on 2. Check if “Dysphagia” IS-A “Condi on” in WordNet
• Wordnet for dysphagia 66 Dan Jurafsky
Rela ons for scoring
• Q: This hockey defenseman ended his career on June 5, 2008 • Passage: On June 5, 2008, Wesley announced his re rement a er his 20th NHL season
• Ques on and passage have very few keywords in common • But both have the Dbpedia rela on ActiveYearsEndDate() !
67 Dan Jurafsky Temporal Reasoning for Scoring Candidates • Rela on databases • (and obituaries, biographical dic onaries, etc.) • IBM Watson ”In 1594 he took a job as a tax collector in Andalusia” Candidates: • Thoreau is a bad answer (born in 1817) • Cervantes is possible (was alive in 1594)
68 Dan Jurafsky Geospa al knowledge (containment, direc onality, borders) • Beijing is a good answer for ”Asian city” • California is ”southwest of Montana” • geonames.org:
69 Dan Jurafsky Text-retrieval-based answer scorer • Generate a query from the ques on and retrieve passages • Replace the focus in the ques on with the candidate answer • See how well it fits the passages. • Robert Redford and Paul Newman starred in this depression-era gri er flick • Robert Redford and Paul Newman starred in The S ng
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Stage 4: Answer Merging and Scoring
• Now we have a list candidate answers each with a score vector • J.F.K [.5 .4 1.2 33 .35 … ]! • John F. Kennedy [.2 .56 5.3 2 …]! • Merge equivalent answers: J.F.K. and John F. Kennedy • Use Wikipedia dic onaries that list synonyms: • JFK, John F. Kennedy, John Fitzgerald Kennedy, Senator John F. Kennedy, President Kennedy, Jack Kennedy • Use stemming and other morphology
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Stage 4: Answer Scoring
• Build a classifier to take answers and a score vector and assign a probability • Train on datasets of hand-labeled correct and incorrect answers.
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Ques on Answering in Watson (Deep QA)