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March 2011

Local and Global Algorithms for

Disambiguation to Wikipedia

Lev Ratinov1, Dan Roth1, Doug Downey2, Mike Anderson3

1University of Illinois at Urbana-Champaign2Northwestern University3Rexonomy

Information overload

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Organizing knowledge

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997..

Chicago VIII was one of the early 70s-era Chicago albums to catch myear, along with Chicago II.

Cross-document co-reference resolution

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997..

Chicago VIII was one of the early 70s-era Chicago albums to catch myear, along with Chicago II.

Reference resolution: (disambiguation to Wikipedia)

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997..

Chicago VIII was one of the early 70s-era Chicago albums to catch myear, along with Chicago II.

The “reference” collection has structure

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997..

Chicago VIII was one of the early 70s-era Chicago albums to catch myear, along with Chicago II.

Used_In

Is_aIs_a

Succeeded

Released

Analysis of Information Networks

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997..

Chicago VIII was one of the early 70s-era Chicago albums to catch myear, along with Chicago II.

Here – Wikipedia as a knowledge resource …. but we can use other resources

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Used_In

Is_aIs_a

Succeeded

Released

Talk outline

High-level algorithmic approach. Bi-partite graph matching with global and local inference.

Local Inference. Experiments & Results

Global Inference. Experiments & Results

Results, Conclusions

Demo

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Problem formulation - matching/ranking problem

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Text Document(s)—News, Blogs,…

Wikipedia Articles

Local approach

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Γ is a solution to the problem A set of pairs (m,t)

m: a mention in the document t: the matched Wikipedia Title

Text Document(s)—News, Blogs,…

Wikipedia Articles

Local approach

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Γ is a solution to the problem A set of pairs (m,t)

m: a mention in the document t: the matched Wikipedia Title

Local score of matchingthe mention to the title

Text Document(s)—News, Blogs,…

Wikipedia Articles

Local + Global : using the Wikipedia structure

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A “global” term – evaluating how good the structure of

the solution is

Text Document(s)—News, Blogs,…

Wikipedia Articles

Can be reduced to an NP-hard problem

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Text Document(s)—News, Blogs,…

Wikipedia Articles

A tractable variation

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1. Invent a surrogate solution Γ’; • disambiguate each mention

independently.2. Evaluate the structure based on pair-

wise coherence scores Ψ(ti,tj)

Text Document(s)—News, Blogs,…

Wikipedia Articles

Talk outline

High-level algorithmic approach. Bi-partite graph matching with global and local inference.

Local Inference. Experiments & Results

Global Inference. Experiments & Results

Results, Conclusions

Demo

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I. Baseline : P(Title|Surface Form)

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P(Title|”Chicago”)

II. Context(Title)

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Context(Charcoal)+=“a font called __ is used to”

III. Text(Title)

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Just the text of the page (one per title)

Putting it all together

City Vs Font: (0.99-0.0001, 0.01-0.2, 0.03-0.01) Band Vs Font: (0.001-0.0001, 0.001-0.2, 0.02-0.01) Training ranking SVM:

Consider all title pairs. Train a ranker on the pairs (learn to prefer the correct solution). Inference = knockout tournament. Key: Abstracts over the text – learns which scores are important.

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ScoreBaseline

ScoreContext

ScoreText

Chicago_city 0.99 0.01 0.03

Chicago_font 0.0001 0.2 0.01

Chicago_band 0.001 0.001 0.02

Example: font or city?

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

Lexical matching

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

Cosine similarity,TF-IDF weighting

Ranking – font vs. city

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

0.5 0.2 0.1 0.8

0.3 0.2 0.3 0.5

Train a ranking SVM

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

(0.5, 0.2 , 0.1, 0.8)

(0.3, 0.2, 0.3, 0.5)

[(0.2, 0, -0.2, 0.3), -1]

Scaling issues – one of our key contributions

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

Scaling issues

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

This stuff is big, and is loaded into the memory

from the disk

Improving performance

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It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”.

Text(Chicago_city), Context(Chicago_city)

Text(Chicago_font), Context(Chicago_font)

Rather than computing TF-IDF weighted cosine

similarity, we want to train a classifier on the fly. But

due to the aggressive feature pruning, we

choose PrTFIDF

Performance (local only): ranking accuracy

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Dataset Baseline(solvable)

+Local TFIDF(solvable)

+Local PrTFIDF(solvable)

ACE 94.05 95.67 96.21

MSN News 81.91 84.04 85.10

AQUAINT 93.19 94.38 95.57

Wikipedia Test 85.88 92.76 93.59

Talk outline

High-level algorithmic approach. Bi-partite graph matching with global and local inference.

Local Inference. Experiments & Results

Global Inference. Experiments & Results

Results, Conclusions

Demo

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Co-occurrence(Title1,Title2)

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The city senses of Boston and Chicago

appear together often.

Co-occurrence(Title1,Title2)

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Rock music and albums appear together often

Global ranking

How to approximate the “global semantic context” in the document”? (What is Γ’?) Use only non-ambiguous mentions for Γ’ Use the top baseline disambiguation for NER surface forms. Use the top baseline disambiguation for all the surface forms.

How to define relatedness between two titles? (What is Ψ?)

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Ψ : Pair-wise relatedness between 2 titles:

Normalized Google Distance

Pointwise Mutual Information

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What is best the Γ’? (ranker accuracy, solvable mentions)

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Dataset Baseline Baseline+Lexical

Baseline+GlobalUnambiguous

Baseline+GlobalNER

Baseline+Global, AllMentions

ACE 94.05 94.56 96.21 96.75

MSN News 81.91 84.46 84.04 88.51

AQUAINT 93.19 95.40 94.04 95.91

Wikipedia Test 85.88 89.67 89.59 89.79

Results – ranker accuracy (solvable mentions)

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Dataset Baseline Baseline+Lexical

Baseline+GlobalUnambiguous

Baseline+GlobalNER

Baseline+Global, AllMentions

ACE 94.05 96.21 96.75

MSN News 81.91 85.10 88.51

AQUAINT 93.19 95.57 95.91

Wikipedia Test 85.88 93.59 89.79

Results: Local + Global

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Dataset Baseline Baseline+Lexical

Baseline+Lexical+Global

ACE 94.05 96.21 97.83

MSN News 81.91 85.10 87.02

AQUAINT 93.19 95.57 94.38

Wikipedia Test 85.88 93.59 94.18

Talk outline

High-level algorithmic approach. Bi-partite graph matching with global and local inference.

Local Inference. Experiments & Results

Global Inference. Experiments & Results

Results, Conclusions

Demo

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Conclusions:

Dealing with a very large scale knowledge acquisition and extraction problem

State-of-the-art algorithmic tools that exploit using content & structure of the network.

Formulated a framework for Local & Global reference resolution and disambiguation into knowledge networks

Proposed local and global algorithms: state of the art performance. Addressed scaling issue: a major issue. Identified key remaining challenges (next slide).

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We want to know what we don’t know

Not dealt well in the literature “As Peter Thompson, a 16-year-old hunter, said ..” “Dorothy Byrne, a state coordinator for the Florida Green Party…”

We train a separate SVM classifier to identify such cases. The features are: All the baseline, lexical and semantic scores of the top candidate. Score assigned to the top candidate by the ranker. The “confidence” of the ranker on the top candidate with respect to

second-best disambiguation. Good-Turing probability of out-of-Wikipedia occurrence for the

mention. Limited success; future research.

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Comparison to the previous state of the art (all mentions, including OOW)

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Dataset Baseline Milne&Witten Our System-GLOW

ACE 69.52 72.76 77.25

MSN News 72.83 68.49 74.88

AQUAINT 82.64 83.61 83.94

Wikipedia Test 81.77 80.32 90.54

Demo

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