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1/55 Web-Scale Knowledge Discovery and Population from Unstructured Data ACLCLP-IR2010 Workshop Heng Ji Computer Science Department Queens College and.

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Presentation on theme: "1/55 Web-Scale Knowledge Discovery and Population from Unstructured Data ACLCLP-IR2010 Workshop Heng Ji Computer Science Department Queens College and."— Presentation transcript:

1 1/55 Web-Scale Knowledge Discovery and Population from Unstructured Data ACLCLP-IR2010 Workshop Heng Ji Computer Science Department Queens College and the Graduate Center City University of New York December 3, 2010

2 2/55 Outline Motivation of Knowledge Base Population (KBP) KBP2010 Task Overview Data Annotation and Analysis Evaluation Metrics A Glance of Evaluation Results CUNY-BLENDER KBP2010 Discussions and Lessons Preview of KBP2011  Cross-lingual (Chinese-English) KBP  Temporal KBP

3 3/55 Limitations of Traditional IE/QA Tracks Traditional Information Extraction (IE) Evaluations (e.g. Message Understanding Conference /Automatic Content Extraction programs)  Most IE systems operate one document a time; MUC-style Event Extraction hit the 60% ‘performance ceiling’  Look back at the initial goal of IE Create a database of relations and events from the entire corpus Within-doc/Within-Sent IE was an artificial constraint to simplify the task and evaluation Traditional Question Answering (QA) Evaluations  Limited efforts on disambiguating entities in queries  Limited use of relation/event extraction in answer search

4 4/55 The Goal of KBP Hosted by the U.S. NIST, started from 2009, supported by DOD, coordinated by Heng Ji and Ralph Grishman in 2010, 55 teams registered, 23 teams participated Our Goal  Bridge IE and QA communities  Promote research in discovering facts about entities and expanding a knowledge source What’s New & Valuable  Extraction at large scale (> 1 million documents) ;  Using a representative collection (not selected for relevance);  Cross-document entity resolution (extending the limited effort in ACE);  Linking the facts in text to a knowledge base;  Distant (and noisy) supervision through Infoboxes;  Rapid adaptation to new relations;  Support multi-lingual information fusion (KBP2011);  Capture temporal information (KBP2011) All of these raise interesting and important research issues

5 5/55 Knowledge Base Population (KBP2010) Task Overview

6 6/55 KBP Setup Knowledge Base (KB)  Attributes (a.k.a., “slots”) derived from Wikipedia infoboxes are used to create the reference KB Source Collection  A large corpus of newswire and web documents (>1.3 million docs) is provided for systems to discover information to expand and populate KB

7 7/55 Entity Linking: Create Wiki Entry? Query = “James Parsons” NIL

8 8/55 Entity Linking Task Definition Involve Three Entity Types  Person, Geo-political, Organization Regular Entity Linking  Names must be aligned to entities in the KB; can use Wikipedia texts Optional Entity linking  Without using Wikipedia texts, can use Infobox values Query Example Jim Parsons eng-NG

9 9/55 Slot Filling: Create Wiki Infoboxes? School Attended: University of Houston Jim Parsons eng-WL PER E per:date_of_birth per:age per:country_of_birth per:city_of_birth

10 10/55 Regular Slot Filling Person Organization per:alternate_namesper:titleorg:alternate_names per:date_of_birthper:member_oforg:political/religious_affiliation per:ageper:employee_oforg:top_members/employees per:country_of_birthper:religionorg:number_of_employees/members per:stateorprovince_of_birthper:spouseorg:members per:city_of_birthper:childrenorg:member_of per:originper:parentsorg:subsidiaries per:date_of_deathper:siblingsorg:parents per:country_of_deathper:other_familyorg:founded_by per:stateorprovince_of_deathper:chargesorg:founded per:city_of_deathorg:dissolved per:cause_of_deathorg:country_of_headquarters per:countries_of_residenceorg:stateorprovince_of_headquarters per:stateorprovinces_of_residenceorg:city_of_headquarters per:cities_of_residenceorg:shareholders per:schools_attendedorg:website

11 11/55 Data Annotation and Analysis

12 12/55 Data Annotation Overview Entity Linking Corpus Genre/SourceSize (entity mentions) PersonOrganizationGPE Training2009 Training Web data500 EvaluationNewswire500 Web data250 Slot Filling Corpus TaskSourceSize (entities) PersonOrganization TrainingRegular Task2009 Evaluation Participants LDC25 Surprise Task2010 LDC16 EvaluationRegular TaskLDC50 Surprise TaskLDC20 Source collection: about 1.3 million newswire docs and 500K web docs, a few speech transcribed docs

13 13/55 Entity Linking Inter-Annotator Agreement Annotator 1 Annotator 3 Annotator 2 Entity Type#Total QueriesAgreement RateGenre#Disagreed Queries Person %Newswire4 Web Text1 Geo-political6487.5%Newswire3 Web Text5 Organization %Newswire3 Web Text1

14 14/55 14/35 Slot Filling Human Annotation Performance Evaluation assessment of LDC Hand Annotation PerformanceP(%)R(%)F(%) All Slots All except per:top-employee, per:member_of, per:title Why is the precision only 70%?  32 responses were judged as inexact and 200 as wrong answers  A third annotator’s assessment on 20 answers marked as wrong: 65% incorrect; 15% correct; 20% uncertain  Some annotated answers are not explicitly stated in the document  … some require a little world knowledge and reasoning  Ambiguities and underspecification in the annotation guideline  Confusion about acceptable answers  Updates to KBP2010 annotation guideline for assessment

15 15/55 15/23 Slot Filling Annotation Bottleneck The overlap rates between two participant annotators in community are generally lower than 30% Keep adding more human annotators help? No

16 16/55 16/23 Can Amazon Mechanical Turk Help? Given a q, a and supporting context sentence, Turk should judge if the answer is  Y: correct; N: incorrect; U: unsure Result Distribution for 1690 instances Useful Annotations (41.8%) Useless Annotations (58.2%) CasesNumberCasesNumber Y Y Y Y Y230 Y Y Y N N164 N N N N N16 Y Y Y N U165 Y Y Y Y U151 N N N Y Y 158 N N N N U24Y Y N N U171 Y Y Y Y N227 Y Y N U U77 N N N N Y46 Y Y U U U17 Y Y Y U U 13 N N N Y U 72 N N N U U 59N N Y U U 57 Y N U U U 22 Y U U U U 8 N N U U U 11 N U U U U 1 U U U U U 1

17 17/55 17/23 Why is Annotation so hard for Non-Experts? Even for all-agreed cases, some annotations are incorrect… Require quality control Training difficulties QuerySlotAnswerContext Citibank org: top_members /employees Tim Sullivan He and Tim Sullivan, Citibank's Boston area manager, said they still to plan seek advice from activists going forward. International Monetary Fund org: subsidiaries World Bank President George W. Bush said Saturday that a summit of world leaders agreed to make reforms to the World Bank and International Monetary Fund.

18 18/55 18/35 Evaluation Metrics

19 19/55 19/35 Entity Linking Scoring Metric Micro-averaged Accuracy (official metric)  Mean accuracy across all queries Macro-averaged Accuracy  Mean accuracy across all KB entries

20 20/55 20/35 Slot Filling Scoring Metric Each response is rated as correct, inexact, redundant, or wrong (credit only given for correct responses)  Redundancy: (1) response vs. KB; (2) among responses: build equivalence class, credit only for one member of each class Correct = # (non-NIL system output slots judged correct) System = # (non-NIL system output slots) Reference = # (single-valued slots with a correct non-NIL response) + # (equivalence classes for all list-valued slots) Standard Precision, Recall, F-measure

21 21/55 21/35 Evaluation Results

22 22/55 22/35 Top-10 Regular Entity Linking Systems <0.8 correlation between overall vs. Non-NIL performance

23 23/55 23/35 Human/System Entity Linking Comparison (subset of 200 queries) Average among three annotators

24 24/55 24/35 Top-10 Regular Slot Filling Systems

25 25/55 CUNY-BLENDER KBP2010

26 26/55 26/23 Free Base Wikipedia Answers Query Expansion Query QA IE Pattern Matching Answer Validation Answer Filtering Statistical Answer Re-ranking Cross-System & Cross-Slot Reasoning External KBs Answer Validation Text Mining Inexact & Redundant Answer Removal Priority-based Combination System Overview

27 27/55 27/23 IE Pipeline KBP 2010 slotsACE2005 relations/ events per:date_of_birth, per:country_of_birth, per:stateorprovince_of_birth, per:city_of_birth event: be-born per:countries_of_residence, per:stateorprovinces_of_residence, per:cities_of_residence,per:religion relation:citizen-resident-religion- ethnicity per:school_attendedrelation:student-alum per:member_ofrelation:membership, relation:sports-affiliation per:employee_ofrelation:employment per:spouse, per:children, per:parents, per:siblings, per:other_family relation:family, event: marry, event:divorce per:chargesevent:charge-indict, event:convict Apply ACE Cross-document IE (Ji et al., 2009) Mapping ACE to KBP, examples:

28 28/55 28/23 Pattern Learning Pipeline Selection of query-answer pairs from Wikipedia Infobox  split into two sets Pattern extraction  For each {q,a} pair, generalize patterns by entity tagging and regular expressions e.g. died at the age of Pattern assessment  Evaluate and filter based on matching rate Pattern matching  Combine with coreference resolution Answer Filtering based on entity type checking, dictionary checking and dependency parsing constraint filtering

29 29/55 29/23 QA Pipeline Apply open domain QA system, OpenEphyra (Schlaefer et al., 2007) Relevance metric related to PMI and CCP  Answer pattern probability: P (q, a) = P (q NEAR a): NEAR within the same sentence boundary Limited by occurrence based confidence and recall issues

30 30/55 30/23 More Queries and Fewer Answers Query Template expansion  Generated 68 question templates for organizations and 68 persons Who founded ? Who established ? was created by who? Query Name expansion  Wikipedia redirect links Heuristic rules for Answer Filtering  Format validation  Gazetteer based validation  Regular expression based filtering  Structured data identification and answer filtering

31 31/55 31/23 Motivation of Statistical Re-Ranking Union and voting are too sensitive to the performance of baseline systems  Union guarantees highest recall requires comparable performance  Voting assumes more frequent answers are more likely true (FALSE)  Priority-based combination voting with weights assumes system performance does not vary by slot (FALSE) SlotIEQAPL org:country_of_headquarters org:founded per:date_of_birth per:origin

32 32/55 32/23 Statistical Re-Ranking Maximum Entropy (MaxEnt) based supervised re- ranking model to re-rank candidate answers for the same slot Features  Baseline Confidence  Answer Name Type  Slot Type X System  Number of Tokens X Slot Type  Gazetteer constraints  Data format  Context sentence annotation (dependency parsing, …)  …

33 33/55 33/23 MLN-based Cross-Slot Reasoning Motivation  each slot is often dependent on other slots  can construct new ‘revertible’ queries to verify candidate answers  X is per:children of Y  Y is per:parents of X;  X was born on date Y  age of X is approximately (the current year – Y) Use Markov Logic Networks (MLN) to encode cross-slot reasoning rules  Heuristic inferences are highly dependent on the order of applying rules  MLN can adds a weight to each inference rule integrates soft rules and hard rules

34 34/55 Name Error Examples Faisalabad 's Catholic Bishop John Joseph, who had been campaigning against the law, shot himself in the head outside a court in Sahiwal district when the judge convicted Christian Ayub Masih under the law in Nominal Missing Error Examples  supremo/shepherd/prophet/sheikh/Imam/overseer/oligarchs/Shiites … Intuitions of using lexical knowledge discovered from ngrams  Each person has a Gender (he, she…) and is Animate (who…) Error Analysis on Supervised Model classification errors spurious errors missing errors

35 35/55 Motivations of Using Web-scale Ngrams Data is Power  Web is one of the largest text corpora: however, web search is slooooow (if you have a million queries). N-gram data: compressed version of the web  Already proven to be useful for language modeling  Google N-gram: 1 trillion token corpus (Ji and Lin, 2009)

36 36/55 car 13966, automobile 2954, road 1892, auto 1650, traffic 1549, tragic 1480, motorcycle 1399, boating 823, freak 733, drowning 438, vehicle 417, hunting 304, helicopter 289, skiing 281, mining 254, train 250, airplane 236, plane 234, climbing 231, bus 208, motor 198, industrial 187, swimming 180, training 170, motorbike 155, aircraft 152, terrible 137, riding 136, bicycle 132, diving 127, tractor 115, construction 111, farming 107, horrible 105, one-car 104, flying 103, hit- and-run 99, similar 89, racing 89, hiking 89, truck 86, farm 81, bike 78, mine 75, carriage 73, logging 72, unfortunate 71, railroad 71, work-related 70, snowmobile 70, mysterious 68, fishing 67, shooting 66, mountaineering 66, highway 66, single-car 63, cycling 62, air 59, boat 59, horrific 56, sailing 55, fatal 55, workplace 50, skydiving 50, rollover 50, one-vehicle 48, 48, work 47, single-vehicle 47, vehicular 45, kayaking 43, surfing 42, automobile 41, car 40, electrical 39, ATV 39, railway 38, Humvee 38, skating 35, hang-gliding 35, canoeing 35, , shuttle 34, parachuting 34, jeep 34, ski 33, bulldozer 31, aviation 30, van 30, bizarre 30, wagon 27, two-vehicle 27, street 27, glider 26, " 25, sawmill 25, horse 25, bomb-making 25, bicycling 25, auto 25, alcohol-related 24, snowboarding 24, motoring 24, early-morning 24, trucking 23, elevator 22, horse-riding 22, fire 22, two-car 21, strange 20, mountain-climbing 20, drunk-driving 20, gun 19, rail 18, snowmobiling 17, mill 17, forklift 17, biking 17, river 16, motorcyle 16, lab 16, gliding 16, bonfire 16, apparent 15, aeroplane 15, testing 15, sledding 15, scuba-diving 15, rock-climbing 15, rafting 15, fiery 15, scooter 14, parachute 14, four-wheeler 14, suspicious 13, rodeo 13, mountain 13, laboratory 13, flight 13, domestic 13, buggy 13, horrific 12, violent 12, trolley 12, three-vehicle 12, tank 12, sudden 12, stupid 12, speedboat 12, single 12, jousting 12, ferry 12, airplane 12, unrelated 11, transporter 11, tram 11, scuba 11, common 11, canoe 11, skateboarding 10, ship 10, paragliding 10, paddock 10, moped 10, factory 10

37 37/55 Discovery Patterns (Bergsma et al., 2005, 2008)  (tag=N.*|word=[A-Z].*) tag=CC.* (word=his|her|its|their)  (tag=N.*|word=[A-Z].*) tag=V.* (word=his|her|its|their)  … If a mention indicates male and female with high confidence, it’s likely to be a person mention Gender Discovery from Ngrams Patterns for candidate mentionsmalefemaleneutralplural John Joseph bought/… his/…32000 Haifa and its/… screenwriter published/… his/… it/… is/… fish

38 38/55 Discovery Patterns  Count the relative pronoun after nouns  not (tag=(IN|[NJ].*) tag=[NJ].* (? (word=,)) (word=who|which|where|when) If a mention indicates animacy with high confidence, it’s likely to be a person mention Animacy Discovery from Ngrams Patterns for candidate mentions AnimateNon-Animate whowhenwherewhich supremo24000 shepherd prophet imam oligarchs sheikh

39 39/55 Candidate mention detection  Name: capitalized sequence of <=3 words; filter stop words, nationality words, dates, numbers and title words  Nominal: un-capitalized sequence of <=3 words without stop words Margin Confidence Estimation freq (best property) – freq (second best property) freq (second best property) Confidence (candidate, Male/Female/Animate) >  Full Matching: John Joseph (M:32)  Composite Matching: Ayub (M:87) Masih (M:117)  Relaxed Matching: Mahmoud (M:159 F:13) Hamadan(N:19) Salim(F:13 M:188) Qawasmi(M:0 F:0) Unsupervised Mention Detection Using Gender and Animacy Statistics

40 40/55 Mention Detection Performance MethodsP(%)R(%)F(%) Name Mention Detection Supervised Model Unsupervised Methods Using Ngrams Nominal Mention Detection Supervised Model Unsupervised Methods Using Ngrams Apply the parameters optimized on dev set directly on the blind test set Blind test on 50 ACE05 newswire documents, 555 person name mentions and 900 person nominal mentions

41 41/55 41/23 Impact of Statistical Re-Ranking Pipelines PrecisionRecall F-measure Bottom-upSupervised IE Pattern Matching Top-downQA Priority based Combination Re-Ranking based Combination fold cross-validation on training set Mitigate the impact of errors produced by scoring based on co-occurrence (slot type x sys feature) e.g. the query “Moro National Liberation Front” and answer “1976”did not have a high co-occurrence, but was bumped up by the re-ranker based on the slot type feature org:founded

42 42/55 42/23 Impact of Cross-Slot Reasoning OperationsTotalCorrect(%) Incorrect(%) Removal27788%12% Adding16100%0% Brian McFadden | per:title | singers | “She had two daughter with one of the MK’d Westlife singers, Brian McFadden, calling them Molly Marie and Lilly Sue”

43 43/55 43/35 Slot-Specific Analysis A few slots account for a large fraction of the answers:  per:title, per:employee_of, per:member_of, and org:top_members/employees account for 37% of correct responses For a few slots, delimiting exact answer is difficult … result is ‘inexact’ slot fills  per:charges, per:title (“rookie driver”; “record producer”) For a few slots, equivalent-answer detection is important to avoid redundant answers  per:title again accounts for the largest number of cases. e.g., “defense minister” and “defense chief” are equivalent.

44 44/55 44/35 How much Inference is Needed?

45 45/55 45/35 Why KBP is more difficult than ACE Cross-sentence Inference – non-identity coreference(per:children)  Lahoud is married to an Armenian and the couple have three children. Eldest son Emile Emile Lahoud was a member of parliament between 2000 and Cross-slot Inference (per:children)  People Magazine has confirmed that actress Julia Roberts has given birth to her third child a boy named Henry Daniel Moder. Henry was born Monday in Los Angeles and weighed 8? lbs. Roberts, 39, and husband Danny Moder, 38, are already parents to twins Hazel and Phinnaeus who were born in November 2006.

46 46/55 46/23 Statistical Re-Ranking based Active Learning

47 47/55 Preview of KBP2011

48 48/55 Cross-lingual Entity Linking Query = “ 吉姆. 帕森斯 ”

49 49/55 Cross-lingual Slot Filling Other family: Todd Spiewak Query = “James Parsons”

50 50/55 Cross-lingual Slot Filling Two Possible Strategies  1. Entity Translation (ET) + Chinese KBP  2. Machine Translation (MT) + English KBP Stimulate Research on  Information-aware Machine Translation  Translation-aware Information Extraction  Foreign Language KBP, Cross-lingual Distant Learning

51 51/55 Query: Elizabeth II Slot type: per:cities_of_residence Answer: Gulf XIN | Xinhua News Agency, London, may 10 -according to British media ten, British Queen Elizabeth II did not favour in the Gulf region to return British unit to celebrate the victory in the war. Error Example of SF on MT output

52 52/55 Query Name in Document Not Translated Query: Celine Dion Answer: PER:Origin = Canada British singer, Clinton's plan to Caesar Palace of the ( Central news of UNAMIR in Los Angeles, 15th (Ta Kung Pao) - consider British singer, Clinton ( ELT on John ) today, according to the Canadian and the seats of the Matignon Accords, the second to Las Vegas in the international arena heavyweight.

53 53/55 Answer in Document Not Translated Query: David Kelly Answer: per:schools_attended = Oxford University MT: The 59-year-old Kelly is the flea basket for trapping fish microbiology and internationally renowned biological and chemical weapons experts. He had participated in the United Nations Iraq weapons verification work, and the British Broadcasting Corporation ( BBC ) the British Government for the use of force on Iraq evidence the major sources of information. On, Kelly in the nearby slashed his wrist, and public opinion holds that he " cannot endure the enormous psychological pressure " to commit suicide.

54 54/55 Temporal KBP (Slot Filling)

55 55/55 Temporal KBP Many attributes such as a person’s title and employer, and spouse change over time  Time-stamped data is more valuable  Distinguish static attributes and dynamic attributes  Address the multiple answer problem What representation to require?  Such explicit info rarely provided , > Captures wider range of information

56 56/55 Temporal KBP: scoring Score each element of 4-tuple separately, then combine scores Smoothed score to handle +∞ and -∞ Need rules for granularity mismatches Year vs month vs day Possible Formula (constraint based validation) key = ; answer = if x i is judged as incorrect then otherwise

57 57/55 Need Cross-document Aggregation Query: Ali Larijani; Answer: Iran Doc1: Ali Larijani had held the post for over two years but resigned after reportedly falling out with the hardline Ahmadinejad over the handling of Iran's nuclear case. Doc2: The new speaker, Ali Larijani, who resigned as the country's nuclear negotiator in October over differences with Ahmadinejad, is a conservative and an ardent advocate of Iran's nuclear program, but is seen as more pragmatic in his approach and perhaps willing to engage in diplomacy with the West.

58 58/55 Same Relation Repeat Over Time Query: Mark Buse; Answer: McCain Doc1: NYT_ENG_ LDC2009T13.sgm (seven years, P7Y); (2001, 2001)In his case, it was a round trip through the revolving door: Buse had directed McCain's committee staff for seven years before leaving in 2001 to lobby for telecommunications companies. Doc2:LTW_ENG_ LDC2009T13.sgm (this year, 2008)Buse returned to McCain's office this year as chief of staff.

59 59/55 Require Paraphrase Discovery Query: During when was R. Nicholas Burns a member of the U.S. State Department? Answer: APW_ENG_ LDC2007T07 R. Nicholas Burns, a career foreign service officer in charge of Russian affairs at the National Security Council, is due to be named the new spokesman at the U.S. State Department, a senior U.S. official said Thursday. [APW_ENG_ LDC2009T13 and many other DOCS] The United States is "very pleased by the strength of this resolution" after two years of diplomacy, said R. Nicholas Burns, undersecretary for political affairs at the State Department. NYT_ENG_ LDC2009T13 R. Nicholas Burns, the country's third-ranking diplomat and Secretary of State Condoleezza Rice's right-hand man, is retiring for personal reasons, the State Department said Friday. NYT_ENG_ LDC2009T13 The chief U.S. negotiator, R. Nicholas Burns, who left his job on Friday, countered that the sanctions were all about Iran's refusal to stop enriching uranium, not about weapons. But that argument was a tough sell.

60 60/55 60/23 Related Work Extracting slots for persons and organizations (Bikel et al., 2009; Li et al., 2009; Artiles et al., 2008)  Distant Learning (Mintz et al., 2009) Re-ranking techniques (e.g. Collins et al., 2002; Zhai et al., 2004; Ji et al., 2006) Answer validation for QA (e.g. Magnini et al., 2002; Peatas et al., 2007; Ravichandran et al., 2003; Huang et al., 2009) Inference for Slot Filling (Bikel et al., 2009; Castelli et al., 2010)

61 61/55 Conclusions KBP proves a much more challenging task than traditional IE/QA Brings great opportunity to stimulate research and collaborations across communities An adventure to promote IE to web-scale processing and higher quality Encourage research on cross-document cross-lingual IE Big gains from statistical re-ranking combining 3 pipelines  Information Extraction  Pattern Learning  Question-Answering Further gains from MLN cross-slot reasoning Automatic profiles from SF dramatically improve EL Human-system combination provides efficient answer-key generation  Faster, better, cheaper!

62 62/55 Thank you and Join us: 62

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