Contextual Search and Name Disambiguation in Email using

Graphs

Einat Minkov

William W. Cohen

Andrew Y. Ng

SIGIR-2006

Outline Extended similarity measure using graph walks Instantiation for Email Learning Evaluation

Person Name Disambiguation Threading

Summary and future directions

Object Similarity Textual similarity measures model D-D (or Q:D) similarity

However, in structured data, documents are not isolated.

We are interested in extending the text-based similarity measures to complex structure settings:

Represent structured data as a graph Derive object similarity using lazy graph walks.

We instantiate this framework for Email (a private case of structured data)

Email as a Graph

file1

Chris.germany@enron.com Chris

sent_

from_

email

alias

sent_from

Mgermany@ch2m.com

Melissa Germany

sent_

to_email

sent_to

has_subj_term

yo I’mwork where

you

has_term

On_date1.22.00

• A directed graph

• A node carries an entity type

• An edge carries a relation type

• Edges are bi-directional (cyclic)

• Nodes inter-connect via linked entities.

Email as a Graph

Edge Weights Graph G :

- nodes x, y, z - node types T(x), T(y), T(z)

- edge labels- parameters

Edge weight x y:

Prob. Distribution:

a. Pick an outgoing edge label b. Pick node y uniformly

Graph SimilarityDefined by lazy graph walks over k steps.

Given:

A transition matrix:

Stay probability: (larger values favor shorter paths)

Initial node distribution:

We use this platform to perform SEARCH of related items in the graph:

a query is initial distribution Vq over nodes and a desired output type Tout

Output node distribution:

Relation to IDF Reduce the graph to files and terms only.

One-dimensional search of files, over one step (Query = multiple source term nodes)

A natural IDF filter:terms occurring in multiple files will ‘spread’ their probability mass into small fractions over many file nodes.

file2

term5

term6

term4

term2

term3

term1

term7

file3

file1

Learning Learn how to better rank graph nodes per a particular task.

The parameters can be adjusted using gradient descent methods (Diligenti et-al, IJCAI 2005)

We suggest a re-ranking approach (Collins and Koo, Computational Linguistics, 2005)

take advantage of ‘global’ features

A training example includes: a ranked list of li nodes. Each node represented through m features At least one known correct node

Features will describe the graph walk paths

Path describing Features The full set of paths to a target node in step k can be recovered.

K=0 K=1 K=2

X1

X2

X3

X4

X5

Paths (x3, k=2):

x2 x3

x2 x1 x3

x4 x1 x3

x2 x2 x3

‘Edge unigrams’:was edge type l used in reaching x from Vq.

‘Edge bigrams’:were edge types l1 and l2 used (in that order) in reaching x from Vq.

‘Top edge bigrams’:were edge types l1 and l2 used (in that order) in reaching x from Vq, among the top two highest scoring paths.

Outline Extended similarity measure using graph walks Instantiation for Email Learning Evaluation

Person Name Disambiguation Threading

Summary and future directions

Person Name Disambiguation

Given: a term that is known to be a personal name is not mentioned ‘as is’ in header

(otherwise, easy) Output:

ranked person nodes.

“who is Andy?”

file

andy

file

Person

file

Person

Person

Corpora and Datasets

Example types : Andy Andrew Kai Keiko Jenny Xing

Two-fold problem: Map terms to person nodes (co-occurrence) Disambiguation (context)

Files Nodes Test Train

Mgmt. Game 821 6248 80 26

Sager-E 1632 9753 51 11

Shapiro-R 978 13174 49 11

Corpus Dataset

Methods

4. G: Term + File, Reranked

Re-rank (3), using:

- Path-describing features

- ‘source count’ : do the paths originate from a single/two source nodes

- string similarity

1. Baseline: String matching (& common nicknames)

Find persons that are similar to the name term (Jaro measure)

• Lexical similarity

2. Graph walk: Term

Vq: name term node

• Co-occurrence

3. Graph walk: Term + File

Vq: name term + file nodes

• Co-occurrence

• Ambiguity

• but, incorporates additional noise

Results

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ResultsMAP Δ Acc. Δ

Baseline 49.0 - 41.3 -Graph - T 72.6 48% 61.3 48%Graph - T + F 66.3 35% 48.8 18%Reranked - T 85.6 75% 72.5 76%Reranked - T + F 89.0 82% 83.8 103%

MAP Δ Acc. ΔBaseline 67.5 - 39.2 -Graph - T 82.8 23% 66.7 70%Graph - T + F 61.7 -9% 41.2 5%Reranked - T 83.2 23% 68.6 75%Reranked - T + F 88.9 32% 80.4 105%

MAP Δ Acc. ΔBaseline 60.8 - 38.8 -Graph - T 84.1 38% 63.3 65%Graph - T + F 56.5 -7% 38.8 1%Reranked - T 87.9 45% 65.3 71%Reranked - T + F 85.5 41% 77.6 103%

Mgmt. Game

Enron:Sager-E

Enron:Shapiro-R

Threading There are often irregularities in thread structural information

(Lewis and Knowles, 1997)

Threading can improve message categorization into topical folders (Klimt and Yang, 2004)

Adjacent messages in a thread can be assumed to be most similar to each other in the corpus. An approximation for finding similar messages in a corpus.

Given: a file

Output: ranked file nodes adjacent files in a thread are correct answers

filex

Shared content

Social network

Timeline

The Joint Graph

Threading: experiments1. Baseline: TF-IDF Similarity

Consider all the available information (header & body) as text

2. Graph walk: Uniform weights Vq: file, 2 steps

3. Graph walk: Random weights Vq: file, 2 steps(best out of 10)

4. Graph walk: rerankedRerank the output of (3) using the graph-describing features

Results

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all no reply lines no reply lines no subj

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all no reply lines no reply lines no subj

Header & BodySubjectReply lines

Header & BodySubject-

Header & Body--

Header & BodySubjectReply lines

Header & BodySubject-

Header & Body--

Mgmt. Game

Enron:Farmer

MA

PM

AP

58.4

73.8

50.2

71.5

36.2

60.3

65.7

79.8

36.1

65.1

Main Contributions Presented an extended similarity measure incorporating

non-textual objects

Perform finite lazy random walks for typed search

A re-ranking paradigm to improve on graph walk results

Instantiation of this framework for Email

Enron Datasets and corpora are available online

Future directions Scalability:

Sampling-based approximation to iterative matrix multiplication

10-step walks on a million-node corpus in 10-15 seconds

Language Model Learning:

Adjust the weights Eliminate noise in contextual/complex queries

Timeline

Related Research IR:

Infinite walks for node centrality Graph walks for query expansion Spreading activation over semantic/association networks

Data Mining Relational data representation

Machine Learning Semi supervised learning in graphs

Thank you! Questions?