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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