Lexical networks lexical centrality and text mining
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Computational Linguistics And Information Retrieval @ Michigan. Lexical networks, lexical centrality, and text mining. Dragomir R. Radev Associate Professor University of Michigan CSE, SI, LING [email protected] The CLAIR gang. This talk is based on joint work with Vahed Qazvinian

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Lexical networks lexical centrality and text mining

Computational

Linguistics

And

Information

Retrieval @ Michigan

Lexical networks, lexical centrality, and text mining

Dragomir R. Radev

Associate Professor

University of Michigan CSE, SI, LING

[email protected]


The clair gang

The CLAIR gang

  • This talk is based on joint work with

    • Vahed Qazvinian

    • Joshua Gerrish

  • With contributions by

    • Arzucan Özgür

    • Güneş Erkan

    • Ahmed Awadallah

    • Bryan Gibson

    • Thuy Vu

    • Xiaodong Shi

    • Mark Joseph

    • Sean Gerrish

    • Alejandro C de Baca

    • Jahna Otterbacher

    • Benjamin Nash

    • Alex Gonopolskiy


Natural language processing

Natural Language Processing


Social data

Social data

  • Blog postings

  • News stories

  • Speeches in Congress

  • Query logs

  • Movie and book reviews

  • Scientific papers

  • Financial reports

  • Query logs

  • Encyclopedia entries

  • Email

  • Chat room discussions

  • Social networking sites


Social data1

Social data

  • Blog postings

  • News stories

  • Speeches in Congress

  • Query logs

  • Movie and book reviews

  • Scientific papers

  • Financial reports

  • Query logs

  • Encyclopedia entries

  • Email

  • Chat room discussions

  • Social networking sites

WHAT DO ALL OF THESE HAVE IN COMMON?


Natural language processing1

Natural language processing

  • Part of speech tagging

  • Prepositional phrase attachment

  • Parsing

  • Word sense disambiguation

  • Document indexing

  • Text summarization

  • Machine translation

  • Question answering

  • Information retrieval

  • Social network extraction

  • Topic modeling


Talk outline

Talk outline

  • Lexical networks

  • Lexical centrality

  • Latent networks

  • Conclusion


Networks

Networks


Lexical networks lexical centrality and text mining

Interleukin-2 receptor pathway

protein interaction network (from HPRD).

Peri et al., Nucleic Acids Res. 2004 January 1; 32(Database issue): D497–D501.

doi: 10.1093/nar/gkh070.


Lexical networks lexical centrality and text mining

The New York Times

May 21, 2005


Lexical networks

Lexical networks


Lexical networks1

Lexical networks

  • A special case of networks where nodes are words or documents and edges link semantically related nodes

  • Other examples:

    • Words used in dictionary definitions

    • Names of people mentioned in the same story

    • Words that translate to the same word


Lexical networks lexical centrality and text mining

Semantic network


Lexical networks lexical centrality and text mining

Dependency network

bought

Meredith

yesterday

apples

green


Lexical networks lexical centrality and text mining

Dependency network


Lexical centrality

Lexical Centrality


What happened

What happened?

  • Red Sox Are World Champs (again)


How many ways to say it

How many ways to say it?

Red Sox Win the World Series, More Titles Might Follow

Back in the Red: World Series crown returns to Boston as Rockies hit the canvas

Fans celebrate Red Sox win

Rockies Vanish In Thin Air

Police Arrest Dozens After Red Sox World Series Win

Red Sox 4, Rockies 3 Boston Sweeps World Series Again

Victory walk leads to dynasty talk

Red Sox Win Baseball's World Series Title by Sweeping Rockies

Boston enjoys sweep smell of success

Sox sweep Rockies to win Series

World Series: Red Sox sweep Rockies

Red Sox cruise to World Series title

Red Sox Are World Champs

Boston sweep Colorado to win World Series

Red Sox Sweep Colorado in World Series

Red Sox Take World Series

How sweep it is! Red Sox breeze to second World Series title

Boston owners dedicate triumph to Red Sox Nation


Lexical networks lexical centrality and text mining

Boston lowers the broom

We Are the Champions: Red Sox 4, Rockies 3

Sweep and red sox for everybody

Two titles four years apart impossible to compare

Red Sox cash in

The Boston Red Sox swept the Colorado Rockies to win the World Series

Red Sox "do little things" to win second title in four years

World Series victory for Red Sox

Boston reigns supreme

Rockies' heads held high despite loss

Rockies just failed to execute

Boston Fans Fill Streets To Celebrate Series Sweep

It's easy to embrace these Red Sox

Young stars lead Red Sox to World Series title

They spend money in Boston, but they win

Boston fans celebrate World Series win; 37 arrests reported

Soxcess started upstairs

Boston sweep is complete

Red Sox sweep 2007 World Series in Denver

Rookies rise to occasion!

Sox sweep World Series again

Poor pitching, poorer hitting doom Rockies

Red Sox top Rockies and sweep to second World Series championship ...

Sweeping off to Boston

Another in a Series of sweeps

Putting an end to baseball as we've known it

Red Sox Sweep Rockies to Capture Second Title in Four Years

Red Sox accomplished the expected, unbelievable

Rockies Find Being Good Isnt Enough

Boston sweep-walks to title

Red Sox play party crashers in Denver

Unhappy ending for Colorado - MLB

Sox on, Rocks off in sweeping win

Timlin gets to ring up another one

Red Sox complete World Series sweep

Wild celebrations in Boston after World Series win

Red Sox seal sweep of Rockies

Red Sox Win World Series

Sox are kings of diamond

Rockies: Sweep, sweep, swept

Red Sox sweep World Series

Monsters of Beantown: Red Sox win Series

Red Sox claim World Series glory

How sweep it is

Red Sox: Dynasty in the making

Red Sox sweep upstart Rockies

Boston Sweeps Colorado To Win World Series

Red Sox sweep Rockies, take World Series

What curse? Red Sox win Series again

Red Sox Win the World Series, More Titles Might Follow

Back in the Red: World Series crown returns to Boston as Rockies hit the canvas

Fans celebrate Red Sox win

Rockies Vanish In Thin Air

Police Arrest Dozens After Red Sox World Series Win

Red Sox 4, Rockies 3 Boston Sweeps World Series Again

Victory walk leads to dynasty talk

Red Sox Win Baseball's World Series Title by Sweeping Rockies

Boston enjoys sweep smell of success

Sox sweep Rockies to win Series

World Series: Red Sox sweep Rockies

Red Sox cruise to World Series title

Red Sox Are World Champs

Boston sweep Colorado to win World Series

Red Sox Sweep Colorado in World Series

Red Sox Take World Series

How sweep it is! Red Sox breeze to second World Series title

Boston owners dedicate triumph to Red Sox Nation

Red Sox win World Series

Short wait for bosox this time

World Series: Red Sox complete sweep of Rockies

It's Leap Year

Boston Red Sox blank Rockies to clinch World Series

Red Sox Sweep Rockies 4-3 In Game 4

Red Sox claim World Series title

Boston Red Sox win World Series, lose image

Sox sweep Rockies for 2nd title in 4 seasons

Red Sox Complete Sweep Of Rockies For World Series Victory

Red sox wrap up world series rout

Rockies feel the pain, but not the shame

Red Sox, tarnished for so long, now baseball's gold standard

Red sox take title

Boston Celebrates World Series Win

Bad news AL, Red Sox built to last

Boston sweeps it Heartbreak is history for Red Sox

Fans Celebrate Red Sox World Series Win

From cursed to charmed: Red Sox sweep World Series

Red Sox Sweep Rockies To Win World Series

Red Sox make Boston jump for joy, Series Champs

Crowds fill streets after Red Sox win

Papelbon, Timlin savoring Series win

Red Sox scale the Rockies

Even Sox fan losing passion for winning

Rookies respond in first crack at the big time

Red Sox Get 2nd World Series Sweep In 4 Years

Red Sox looking like a dynasty

Boston Red Sox are America's team

Red Sox Win 2007 World Series

Believing Pays Off! Sox World Series Champs

Red Sox go from cursed to blessed


List of topics

List of topics

  • Red Sox

  • Sweep

  • World Series

  • Rockies

  • Celebrate

  • Boston

  • Colorado

  • Dynasty

  • 2007

  • Four years

  • Second time

  • 4:3 score

  • Easy

  • Expectations

  • No curse

  • Young players

  • Timlin

  • not (baseball)


Lexrank centrality in text graphs

LexRank – Centrality in Text Graphs

Vertices

Units of text (sentences or documents)

Edges

Pairwise similarity between text

(tf-idf cosine or language model)


Lexrank centrality in text graphs1

LexRank – Centrality in Text Graphs

Intuition

LexRank score is propagated through edges

Central vertices are those that are similar to other central vertices


Lexrank centrality in text graphs2

LexRank – Centrality in Text Graphs

Recurrence Relation

0.3

0.1

0.9

0.3

s

0.5

0.8

Can guarantee solution by allowing “jump” probability d/N.

0.2

0.4

0.2


Lexical networks lexical centrality and text mining

0.01718 Red Sox Win Baseball's World Series Title by Sweeping Rockies

0.01712 Red Sox Sweep Rockies To Win World Series

0.01647 World Series: Red Sox sweep Rockies

0.01630 Red Sox sweep Rockies, take World Series

0.01608 Red Sox 4, Rockies 3 Boston Sweeps World Series Again

0.01597 World Series: Red Sox complete sweep of Rockies

0.01584 Red Sox sweep World Series

0.01579 Red Sox Sweep Colorado in World Series

0.01573 Red Sox Complete Sweep Of Rockies For World Series Victory

0.01531 Red Sox complete World Series sweep

...

0.01057 Boston Red Sox blank Rockies to clinch World Series

0.01052 Red Sox: Dynasty in the making

0.01037 Sox sweep Rockies for 2nd title in 4 seasons

0.01034 Police Arrest Dozens After Red Sox World Series Win

0.01027 Rookies respond in first crack at the big time

0.01018 Rockies: Sweep, sweep, swept

0.01016 Sweeping off to Boston

0.01013 Rookies rise to occasion!

0.01012 Fans celebrate Red Sox win

0.01010 Short wait for bosox this time

...

0.00441 Sox are kings of diamond

0.00414 Rockies just failed to execute

0.00408 Rockies Find Being Good Isnt Enough

0.00407 Rockies' heads held high despite loss

0.00391 Boston lowers the broom

0.00390 Rockies Vanish In Thin Air

0.00390 Poor pitching, poorer hitting doom Rockies

0.00390 Rockies feel the pain, but not the shame

0.00375 Two titles four years apart impossible to compare

0.00362 Boston reigns supreme


Lexical networks lexical centrality and text mining

0.01718 Red SoxWin Baseball's World Series Title by Sweeping Rockies

0.01712 Red Sox Sweep Rockies To Win World Series

0.01647 World Series: Red Sox sweep Rockies

0.01630 Red Sox sweep Rockies, take World Series

0.01608 Red Sox 4, Rockies 3 Boston Sweeps World Series Again

0.01597 World Series: Red Sox complete sweep of Rockies

0.01584 Red Sox sweepWorld Series

0.01579 Red Sox Sweep Colorado in World Series

0.01573 Red Sox Complete Sweep Of Rockies For World Series Victory

0.01531 Red Sox complete World Series sweep

...

0.01057 Boston Red Soxblank Rockies to clinch World Series

0.01052 Red Sox: Dynasty in the making

0.01037 Sox sweep Rockies for 2nd title in 4 seasons

0.01034 Police Arrest Dozens After Red Sox World Series Win

0.01027 Rookies respond in first crack at the big time

0.01018 Rockies: Sweep, sweep, swept

0.01016 Sweeping off to Boston

0.01013 Rookies rise to occasion!

0.01012 Fans celebrate Red Sox win

0.01010 Short wait for bosox this time

...

0.00441 Sox are kings of diamond

0.00414 Rockies just failed to execute

0.00408 Rockies Find Being Good Isnt Enough

0.00407 Rockies' heads held high despite loss

0.00391 Boston lowers the broom

0.00390 Rockies Vanish In Thin Air

0.00390 Poor pitching, poorer hitting doom Rockies

0.00390 Rockies feel the pain, but not the shame

0.00375 Two titles four years apart impossible to compare

0.00362 Boston reigns supreme


Nlp and network analysis

NLP and network analysis


Dependency parsing

Dependency parsing


Lexical networks lexical centrality and text mining

<root>

<root>

<root>

<period>

<period>

<period>

John

John/likes

John/likes/apples

likes

apples

apples

green

green

green

<root>

<root>

<period>

<root>

John

<period>

likes

apples

John/likes/apples/green/<period>

John/likes/apples/green

green

[McDonald et al. 2005]


Lexical networks lexical centrality and text mining

... , sagte der Sprecher bei der Sitzung .

... , rief der Vorsitzende in der Sitzung .

... , warf in die Tasche aus der Ecke .

C1: sagte, warf, rief

C2: Sprecher, Vorsitzende, Tasche

C3: in

C4: der, die

Part of speech tagging

Word sense disambiguation

Document indexing

[Mihalcea et al 2004]

[Mihalcea et al 2004]

[Biemann 2006]

Subjectivity analysis

Semantic class induction

Passage retrieval

relevance

inter-similarity

Q

[Pang and Lee 2004]

[Widdows and Dorow 2002]

[Otterbacher,Erkan,Radev05]


Mavenrank centrality in speech graphs

MavenRank – Centrality in Speech Graphs

Vertices

Speech transcripts from a given topic

Edges

tf-idf cosine similarity (with threshold)

Hypothesis

Key speakers will have speeches with high centrality.


Mavenrank example

MavenRank: Example

Speech Scores

10.13

20.13

30.10

40.19

50.10

60.14

70.08

80.13

Speaker Scores (mean speech score)

10.12

20.15

30.12

Speaker 1

Speeches

3

2

4

Speaker 2

Speeches

1

5

6

8

7

Speaker 3

Speeches


Lexical networks lexical centrality and text mining

Joint work with Kevin Quinn, Burt Monroe, Michael Colaresi and Michael Crespin

Gosnell Prize 2005


Feature extraction from dependency trees

Feature Extraction from Dependency Trees

“The results demonstrated that KaiC interacts rhythmically with KaiA, KaiB, and SasA.”

Path1: KaiC – nsubj – interacts – obj – SasA

Path2: KaiC – nsubj – interacts – obj – SasA – conj_and – KaiA

Path3: KaiC – nsubj – interacts – obj - SasA – conj_and – KaiB

Path4: SasA – conj_and – KaiA

Path5: SasA – conj_and – KaiB

Path6: KaiA - prep_with - SasA – conj_and – KaiB


Path edit kernel

Path Edit Kernel

  • Character–based --> modified as word-based

    • Minimum number of operations (insertion, deletion, or substitution of a single word) to transform the first string to the second

  • Ex:

    • 1. KaiC - subj - interacts - obj - SasA - conj- KaiA

    • 2. KaiC - subj - interacts - obj - SasA - conj – KaiA

      Edit distance = 2 (2 insertions)

      Normalize edit distance: divide to the length of the longer path

    • 2/7 = 0.286

Integrate SVM with path edit kernel

  • Higher performance than results reported so far in the literature


Aimed

AIMED

Best F-score (59.96%) --> TSVM with Path Edit Kernel (higher than previously reported results)


Inferring genes related to prostate cancer

Inferring Genes Related to Prostate Cancer

  • Hypothesis:

    • Genes that are interacting with many genes that are known to be related to prostate cancer are likely to be related to prostate cancer

  • Approach:

    • Extract the interaction network of genes (seed genes) that are known to be related to prostate cancer automatically from the literature

    • Infer new genes related to prostate cancer from the network topology

    • Use eigenvalue centrality to rank gene-prostate cancer associations

  • Hypothesis restatement:

    • Genes central in the constructed network are most probably related to prostate cancer.


Approach

Approach

  • Corpus:

    • PMCOA (PubMed Central Open Access) – full text articles

    • Articles in PMCOA split into sentences and sentences tagged with GeniaTagger

  • Compile seed list of genes known to be related to prostate cancer

    • 20 genes compiled from OMIM (Online Mendelian Inheritance in Man) Database

    • Extend seed gene list with synonyms from HGNC (HUGO Gene Nomenclature Committee) database.

  • Use the automatic interaction extraction pipeline to extract the interaction network of the seed genes and their neighbors (genes interacting with the seed genes).


Seed genes

Seed Genes

  • 20 genes that are reported in OMIM to be related to prostate cancer


Interactions of the seed genes gene names normalized to their hgnc symbols

Interactions of the seed genes(gene names normalized to their HGNC symbols)


Sample extracted interaction sentences

Sample Extracted Interaction Sentences

  • A study by Jin et al. [20] indicated that the association of Tax with hsMAD1, a mitotic spindle checkpoint (MSC) protein, led to the translocation of both MAD1 and MAD2 to the cytoplasm.

  • PTEN is transcriptionally regulated by transcription factors such as p53, Egr-1, NFκB and SMADs, while protein levels and activity are modulated by phosphorylation, oxidation, subcellular localisation, phospholipid binding and protein stability [29].

  • Interestingly, one of these, HPC1, is linked to RNASEL [10,11].

  • In response to DNA damage, the cell-cycle checkpoint kinase CHEK2 can be activated by ATM kinase to phosphorylate p53 and BRCA1, which are involved in cell-cycle control, apoptosis, and DNA repair [1,2].

  • The interactions of RAD51 with TP53, RPA and the BRC repeats of BRCA2 are relatively well understood (see Discussion).

  • The interaction of BRCA2 with HsRad51 is significantly more different to both RadA and RecA (Figure 2c).

  • Max interactor protein, MXI1 (gene L07648) competes for MAX thus negatively regulates MYC function and may play a role in insulin resistance.

  • Mad2 binds to Cdc20, an activator of the anaphase-promoting complex (APC), to inhibit APC activity and arrest cells in metaphase in response to checkpoint activation.


Inferred genes evaluation of top 20 scoring genes

Inferred Genes(evaluation of top-20 scoring genes)

  • 6 are seed genes; 14 genes are inferred to be related to prostate cancer (Check GeneGo Pathway database; if no evidence there, check PubMed literature)

    • 9 genes: marked as being related to prostate cancer by GeneGo Pathway Database

    • 1 gene: Found evidence in PubMed that gene related to prostate cancer

    • 4 genes: no evidence found


Lexical networks lexical centrality and text mining

GIN - Article View

Citation information

Interaction sentences from this article


Other networks

Other networks

  • Diabetes Type I

  • Diabetes Type II

  • Bipolar Disorder


Properties of lexical networks

Properties of lexical networks


Lexical networks lexical centrality and text mining

Dependency network


Lexical networks lexical centrality and text mining

Random network


Analyzing networks

Analyzing networks

  • Properties of networks

    • Clustering coefficient

      • Watts/Strogatz cc = #triangles/#triples

    • Power law coefficient a

    • Diameter (longest shortest path)

    • Average shortest path (ASP)

  • Properties of nodes

    • Centrality: degree, closeness, betweenness, eigenvector


Types of networks

Types of networks

  • Regular networks

    • Uniform degree distribution

  • Random networks

    • Memoryless

    • Poisson degree distribution

    • Characteristic value

    • Low clustering coefficient

    • Large asp

  • Small world networks

    • High transitivity

    • Presence of hubs (memory)

    • High clustering coefficient

      (e.g., 1000 times higher than random)

    • Small ASP

    • Power law degree distribution

      (typical value of a between 2 and 3)


Comparing the dependency graph to a random poisson graph

Comparing the dependency graph to a random (Poisson) graph


Properties of lexical networks1

universe

letter

character

nature

world

actor

Properties of lexical networks

  • Entries in a thesaurus[Motter et al. 2002]

    • c/c0 = 260 (n=30,000)

  • Co-occurrence networks [Dorogovtsev and Mendes 2001, Sole and Ferrer i Cancho 2001]

    • c/c0 = 1,000 (n=400,000)

  • Mental lexicon [Vitevitch 2005]

    • c/c0 = 278 (n=19,340)


  • Experimental data

    Experimental data


    Statistics

    Statistics

    Based on [Mehler 2007]


    Latent networks

    Latent networks


    Semantic similarity distributions

    Semantic similarity distributions


    Simulations

    Simulations

    • D = number of documents: d1…dD

    • V = vocabulary size ~ Zipf()

    • W = size of document in words


    Lexical networks lexical centrality and text mining

    [Teufel and van Halteren 2004]


    Future directions

    Future directions


    Machine translation

    Machine translation

    1 Mr. Speaker, I rise, on this first full sitting day of the 36th Parliament, to reiterate my call to the Ontario government for an independent public inquiry into the July Plastimet fire in Hamilton.

    2 Conservative Premier Mike Harris and his environment and health ministers have backtracked, flip-flopped on their pledges for an inquiry, citing the pathetic excuse of the need for evidence of wrongdoing.

    3 Is it right that the local MPP had to awaken the provincial environment minister at 3 a.m. before the premier would dispatch air monitoring equipment to the toxic fire site?

    4 Why did the province first refuse and then later accept federal government assistance?

    5 There are questions of compliance with the Ontario fire code, inventory lists, security, and locating a recycling plant near a hospital, schools and a high density residential area.

    6 Frustrated with the Harris government smokescreen, my constituents demand an independent public inquiry to clear the smoke and to produce recommendations which might prevent an environmental tragedy like the Plastimet fire from ever happening again.

    1 Monsieur le Président, je profite de cette première journée complète de séance de la 36e législature pour réclamer de nouveau au gouvernement ontarien une enquête publique indépendante sur l'incendie de Plastimet, survenu à Hamilton en juillet.

    2 Le premier ministre conservateur Mike Harris et ses ministres de l'Environnement et de la Santé ont fait volte-face, après s'être engagés à faire une enquête, sous le prétexte pathétique qu'il fallait des preuves de méfait.

    3 Est-il normal que le député provincial de l'endroit ait dû réveiller le ministre de l'Environnement à 3 heures du matin pour que le premier ministre envoie sur les lieux un équipement de surveillance de la qualité de l'air?

    4 Pourquoi la province a-t-elle d'abord refusé, avant de finir par l'accepter, l'aide du gouvernement fédéral?

    5 Il y a lieu de se poser des questions sur le respect du code ontarien des incendies, les listes d'inventaire, la sécurité, et la décision d'implanter une usine de recyclage à proximité d'un hôpital, d'écoles et d'une zone résidentielle à forte densité.

    6 Exaspérés par l'écran de fumée derrière lequel le gouvernement Harris se retranche, mes électeurs réclament une enquête publique indépendante pour dissiper tout ce qu'il y a de trouble dans cette affaire et formuler des recommandations qui aideront peut-être à prévenir d'autres catastrophes écologiques comme l'incendie de Plastimet.


    Lexical networks lexical centrality and text mining

    1

    fire

    incendie

    2

    premier

    first

    premier

    3

    ministre

    4

    abord

    5

    écran

    smoke

    screen

    fumée

    6


    Final notes

    Final notes


    Funding sources

    Funding sources

    BlogoCenter: Infrastructure for Collecting, Mining and Accessing Blogs

    NSF

    joint project with UCLA

    DHB: The dynamics of Political Representation and Political Rhetoric

    NSF

    joint project with Harvard, Michigan State U. Penn. State U., U. of Georgia

    National center for integrative bioinformatics

    NIH

    RI: iOPENER—A Flexible Framework to Support Rapid Learning in Unfamiliar Research Domains

    NSF

    joint project with Maryland

    Representing and Acquiring Knowledge of Genome Regulation

    NIH (NLM)

    Probabilistic and link-based Methods for Exploiting Very Large Textual Repositories

    NSF

    Collaborative research: semantic entity and relation extraction from Web-scale text document collections

    NSF (Human Languages and Communications Program)

    ITR/IM: Information Fusion Across Multiple Text Sources: A Common Theory

    NSF (Information Technology Research Program)


    Lexical networks lexical centrality and text mining

    URL

    • http://www.eecs.umich.edu/~radev

    • http://www.clairlib.org


    Clairlib

    Clairlib


    Clairlib the clair library

    Imported  

    Parsing

    Stemming

    Sentence segmentation

    Web Page Download

    Web Crawling

    Clairlib: The Clair Library

    • Native

      • Tokenization

      • Summarization

      • LexRank

      • Biased LexRank

      • Document Clustering

      • Document Indexing

      • PageRank

      • Web Graph and Network Analysis


    Lexical networks lexical centrality and text mining

    Computational

    Linguistics

    And

    Information

    Retrieval @ Michigan

    THANK YOU!


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