A Probabilistic Model for Canonicalizing Named Entity Mentions. Dani Yogatama Yanchuan Sim Noah A. Smith

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1 A Probabilistic Model for Canonicalizing Named Entity Mentions Dani Yogatama Yanchuan Sim Noah A. Smith

2 Introduction Model Experiments Conclusions Outline

3 Introduction Model Experiments Conclusions Outline

4 Introduction This work: building a computational model for canonicalizing named entity mentions in texts into a table (Eisenstein et al., 2011) Rows represent entities Columns represent attributes (or parts of attributes) of entities

5 Introduction Political figures Sports figures

6 Input Problem definition A collection of mentions found by a named entity recognizer, along with their contexts Barack Obama accused his November election rival Mitt Romney of having outsourced jobs to India and China. President Obama will address... Mentions: Barack Obama, Mitt Romney, President Obama,... A few seed examples (e.g., tables in the previous slide) Output A named entity table

7 Input Problem definition A collection of mentions found by a named entity recognizer, along with their contexts Barack Obama accused his November election rival Mitt Romney of having outsourced jobs to India and China. President Obama will address... Mentions: Barack Obama, Mitt Romney, President Obama,... A few seed examples (e.g., tables in the previous slide) Output A named entity table

8 Input Problem definition A collection of mentions found by a named entity recognizer, along with their contexts Barack Obama accused his November election rival Mitt Romney of having outsourced jobs to India and China. President Obama will address... Mentions: Barack Obama, Mitt Romney, President Obama,... A few seed examples (e.g., tables in the previous slide) Output A named entity table

9 Input Problem definition A collection of mentions found by a named entity recognizer, along with their contexts Barack Obama accused his November election rival Mitt Romney of having outsourced jobs to India and China. President Obama will address... Mentions: Barack Obama, Mitt Romney, President Obama,... A few seed examples (e.g., tables in the previous slide) Output A named entity table

10 Introduction Model Experiments Conclusions Outline

11 Model Generate table entries. For each column j : Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

12 Model Generate table entries. For each column j : 1 Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

13 Model Generate table entries. For each column j : 1 2 Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

14 Model Generate table entries. For each column j : Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

15 Model Generate table entries. For each column j : Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

16 Model Generate table entries. For each column j : Barack Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

17 Model Generate table entries. For each column j : Barack John Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

18 Model Generate table entries. For each column j : Barack John Mitt Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

19 Model Generate table entries. For each column j : Barack Obama John Mitt Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

20 Model Generate table entries. For each column j : Barack Obama John McCain Draw a multinomial distribution over vocabulary j DP( j,g 0 ) Mitt Romney For each row i: Draw an entry x i,j M( j )

21 Model Generate table entries. For each column j : Barack Obama Mr. John McCain Draw a multinomial distribution over vocabulary j DP( j,g 0 ) Mitt Romney For each row i: Draw an entry x i,j M( j )

22 Model Generate table entries. For each column j : Barack Obama Mr. John McCain Sen. Mitt Romney Gov. Draw a multinomial distribution over vocabulary j DP( j,g 0 ) For each row i: Draw an entry x i,j M( j )

23 Model Barack Obama Mr. John McCain Sen. Mitt Romney Gov.... accused his November election rival Mitt Romney of having outsourced jobs to...

24 Model Generate the distribution over rows (entities) GEM( ) Generate context distribution for each row r Dir( )

25 Model m... < _> { } < _>... r =3 s 1 = of For s 2 each =having mention token m: Draw an entity/row s 1 = r rival s 2 = election Draw context words s m,t M( r )

26 Model... < _> { } <of >... r =3 s 1 = of For s 2 each =having mention token m: Draw an entity/row s 1 = rrival s 2 = election Draw context words s m,t M( r )

27 Model... < _> { } <of having _>... r =3 s 1 = of s 2 =having s 1 = rival s 2 = election

28 Model... < _> { } of having outsourced jobs to... r =3 s 1 = of s 2 =having s 1 = rival s 2 = election

29 Model... accused his November election rival { } of having outsourced jobs to... r =3 s 1 = of s 2 =having s 1 = rival s 2 = election

30 Model For each word in the mention m, given some observed features Choose a column f m,` c 1 Z exp( > c f m,`) With probability 1, set the word Our model is both Bayesian and discriminative w m,` = x r,c

31 Model... accused his November election rival { } of having outsourced jobs to binary features to help column disambiguation: Is it the first word in the mention? Does it end with a period? Is it (a part of) a person entity? 1 Is it (a part of) an organization entity?... w m,` = x r,c

32 Model... accused his November election rival { } of having outsourced jobs to... r =3 1 w m,` = x r,c

33 Model... accused his November election rival { } of having outsourced jobs to... r =3 c 1 =1 1 w m,` = x r,c

34 Model... accused his November election rival { } of having outsourced jobs to... r =3 c 1 =1 c 2 =2 1 w m,` = x r,c

35 Model Barack Obama Mr. John McCain Sen. Mitt Romney Gov. r =3 c 1 =1 c 2 =2... accused his November election rival { } of having outsourced jobs to...

36 Model Barack Obama Mr. John McCain Sen. Mitt Romney Gov. r =3 c 1 =1... accused his November election rival Mitt {_} of having outsourced jobs to...

37 Model Barack Obama Mr. John McCain Sen. Mitt Romney Gov. r =3 c 2 =2... accused his November election rival Mitt Romney of having outsourced jobs to...

38 Learning and inference E-step Collapsed (dashed variables) Gibbs sampling to obtain distributions over row and column indices for every mention Column indices for all words in a mention are sampled together Faster mixing

39 Learning and inference Empirical Bayesian estimation and contrastive estimation M-step Obtain estimates for the parameters (doubly-circled variables)

40 Model comparisons Our model builds on the approach of Eisenstein et al., 2011, but also: Incorporates context of the mention Conditions against shape features Is learned using elements of Bayesian inference with conditional estimation

41 Model comparisons Our model

42 Model comparisons Our model

43 Model comparisons No features apple Different column distribution Eisenstein et al, 2011 No context generations

44 Introduction Model Experiments Conclusions Outline

45 Datasets Politics Sports #docs 3, #mentions 10,647 13,813 #unique mentions

46 Model comparison Hierarchical clustering baseline Previous work by Eisenstein et al., 2011

47 Evaluations Row evaluation How well a model disambiguates entities and merges mentions of the same entity Column evaluation How well a model relates words used in different mentions

48 Evaluation metric output table Barack Obama Mr. Pres. gold-standard table Barack Obama Mr. Pres. Romney Mr. John McCain Mr. Sen. John Obama Mr. Sen. Mitt Romney Mr.

49 Evaluation metric output table gold-standard table Barack Obama Mr. Pres. Barack Obama Mr. Pres. Romney Mr. John McCain Mr. Sen. John Obama Mr. Sen. Mitt Romney Mr. Find a maximum bipartite matching between rows in an output table and a gold-standard table

50 Evaluation metric Romney Mr. Mitt Romney Mr. response score = 2/2 = 1 Compute a precision-like (response score) score

51 Evaluation metric Romney Mr. Mitt Romney Mr. reference score = 2/3 = 0.67 Compute a recall-like (reference score) score

52 Results (politics) Row Column

53 Output analysis (politics) Mention examples: John McCain, Sen. John McCain, Mr. McCain black: seed examples blue: correct entries red: wrong entries

54 Output analysis (politics) Mention examples: John McCain, Sen. John McCain, Mr. McCain black: seed examples blue: correct entries red: wrong entries

55 Output analysis (politics) Mention examples: John McCain, Sen. John McCain, Mr. McCain black: seed examples blue: correct entries red: wrong entries

56 Results (sports) Row Column

57 Output analysis (sports) Mention examples: Kobe Bryant, Los Angeles Lakers black: seed examples blue: correct entries red: wrong entries

58 Introduction Model Experiments Conclusions Outline

59 Conclusions A statistical model for canonicalizing named entities into a table It adapts to different tasks depending on its input and seeds It improves over the state-of-the-art performance on two corpora

60 Thanks!

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