Linking and Summarizing Information on Entities Presented by Min-Yen Kan Web IR / NLP Group (WING)...

Linking and Summarizing Information on Entities

Presented by

Min-Yen Kan

Web IR / NLP Group (WING)

Department of Computer ScienceNational University of Singapore, Singapore

This talk archived as http://wing.comp.nus.edu.sg/~kanmy/talks/080407-nihLMC.htm

http://wing.comp.nus.edu.sg/~kanmy/talks/0804007-nihLMC.htm

2NIH Lister Hill Medical Center

Entity Linkage using the Web and Graph-based Update Summarization

Singapore, the garden city• 4M+ people, sandwiched between Malaysia and Indonesia

• 50 km from the equator: hot and humid year-long

• Known for: urban planning, fondness for acronyms and aversion to bubble gum litterers :-D

WING @ NUShttp://wing.comp.nus.edu.sg

• 1 postdoc, 6 Ph.D. students, 5 undergraduates • Projects of in natural language processing, digital libraries, and information retrieval.

http://wing.comp.nus.edu.sg/



Entity Centric Information Management“Collate all studies on SBP2 that new findings in the last year.”

“Oh, I meant the PROTEIN SBP2, not the gene.”

“What other proteins does SBP2 bind to?”

“Tell me more about the contradiction from previous results.”

“Which Miller did the study on SBP2 in 2002?”



Entity Centric Information Management

Two consequences to discuss today:

• LinkageJoint work with Yee Fan TAN, Dongwon LEE (PSU) et al.

• SummarizationJoint work with Ziheng LIN et al.



What’s Entity Linkage?Aggregating data on an object together from heterogeneous resources

Problem: Entity names are ambiguous!

–Medical terms–Person names–Products–Customer records

These problems exist even when we have controlled vocabulary and lexicons (Specialist, UMLS, MeSH)

By UV cross-linking and immunoprecipitation, we show that SBP2 specifically binds selenoprotein mRNAs both in vitro and in vivo.

The SBP2 clone used in this study generates a 3173 nt transcript (2541 nt of coding sequence plus a 632 nt 3’ UTR truncated at the polyadenylation site).

Gene

Protein



Examples of Split RecordsDongwon Lee, 110 E. Foster

Ave. #410, State College, PA, 16802

Honda Fix

Joint Conf. on Digital Libraries

Apple iPod Nano 4GB

Entity Linkage

LEE Dong, 110 East Foster Avenue Apartment 410, University Park, PA 16802-2343

Honda Jazz

JCDL

4GB iPod nano 4GB

De-duplicationIronic, isn’t it?



All over the web!

Jeffrey D. Ullman(Stanford University)



Record linkage, formally defined• Input

– Two lists of records, A and B

• Output– For each record a in A and for each record b in B,does a and b refer to the same entity?

• Note: – Entities do not come with unique identifiers– To disambiguate (deduplicate) items in a single list L, we set A = B = L



Talk Outline

• Linkage using the Web– Introduction

>> Record linkage using internal knowledgeString matching

Classification or clustering

Graphical formalisms

Blocking

– Record linkage using search engines

• Update Summarization



Fellegi-Sunter model

* true matches

○ true non-matches

false matchesfalse non-matches

no-decision region(hold for human review)

designate asdefinite match

designate asdefinite non-match

Similarity (a, b)

Fre

que

ncy

of S

imila

rity



String matching

•String similarity–Strings as ordered sequences

Edit distance

Jaro and Jaro-Winkler

–Strings as unordered setsJaccard similarity

Cosine similarity

•Abbreviation matching– Pattern detection: e.g. “National Institute of Health (NIH)”

([a], [b], [c]) ≠ ([c], [b], [a])

{[a], [b], [c]} = {[c], [b], [a]}



Machine Learning– Create features

String similarity, relationships (e.g. collaborators)

– Then learn a modelNaïve Bayes, Support Vector Machine, K-means,

Agglomerative Clustering, …

Yoojin Hong, Byung-Won On and Dongwon Lee. SystemSupport for Name Authority Control Problem in

Digital Libraries: OpenDBLP Approach. ECDL 2004.

Sudha Ram, Jinsoo Park and Dongwon Lee. DigitalLibraries for the Next Millennium: Challenges and

Research Directions. Information Systems Frontiers 1999.

Same Person?



Graphical Methods: Social network analysis

• Nodes: entities • Edges: relationships

Y. Wang

M.-Y. Kan

D. Hsu

J. C. Latombe

A. Dhanik

Y. F. TanL. Qiu

T.-S. Chua

T.-H. Chiang

H. CuiAnalysis

Connected componentsDistance between nodesNode/edge centralityCliquesBipartite subgraphs…



Talk Outline

• Linkage using the Web– Introduction

– Record linkage using internal knowledge

>> Record linkage using search enginesSearch Engine Features

Adaptive Queries

Query Probing

• Update Summarization



Record linkage using search engines

Previously…– We assumed input data records contain sufficient information to perform linkage

What if…– There is insufficient or only noisy information?

– e.g., linking short forms to long forms

Ask other people!– I.e., consult external (vs. internal) sources of knowledge

– Use web as collective knowledge base



Anatomy of Search Engine Results

Number of results

Ranked list

Snippet

URL

Title

Web page

Programmatically accessible through APIs



Derivable Features• Counts

– Co-occurrence measure between count(q1), count(q2) and count(q1 and q2)

• Hyperlinkage – Count of web pages of q1 point to pages of q2, and vice versa?– Incorporate additional indirect links with less weight(e.g., q 1 p q2)

• Snippets or web pages– (Cosine) similarity using tokens

– Counts of specific terms

e.g. number of snippets for q1 containing the string q2

– Further natural language processing



Web page features

• Named entities (NE)– We consider people, organizations, locations

– Each NE token a feature

• NE-targeted (NE-T)– Motivation: middle names and titles

– For NEs having a token of target name• Extract tokens that are not in target name as features

Born Edward Charles Morrice Fox in Chelsea,London…

Charles, Chelsea, Morrice,Edward, Fox, London, …

Charles, Morrice, …



Using URLs

Where web pages are located is also useful

Hypothesis: If web pages of q1 and web pages of q2 overlap a lot, q1 and q2 are the same entity

Measure this using URL / Host information

• Caveat: Not all hosts are equally telling

– citeseer vs. harvard.edu for author names – pubmed vs. diabetes-info.com for diabetic terms

• Solution: Weight by Inverse Host Frequency



URL Features (cont.)

• Page URLs

Hypothesis: URL itself tells quite a lot• Home page of “lindek”• CS department, University of Alberta, Canada

– MeURLin (Kan and Nguyen Thi, 2005)• Tokens (http, www, cs, ualberta, ca, lindek)• URI parts (scheme:http, hostname:cs, user:lindek, …)• N-grams (ca ualberta, uaberta cs, cs www, www lindek)• Length of tokens• …

http://www.cs.ualberta.ca/~lindek/



Web search engine linkageTest whether q1 and q2 should be linked

Hypothesis: Web pages of q1 and web pages of q2 share some representative data I

Similar to disconnected triples:

“Jeffrey D. Ullman” = 384K pgs“Jeffrey D. Ullman” + “aho” =174K pgs

“J. Ullman” = 124K pgs“J. Ullman” + “aho” = 41K pgs

“Shimon Ullman” = 27.3K pgs“Shimon Ullman” + “aho”= 66 pgs

q2

q1



Evaluation - Full web pages in WEPS

• Goal– To compare the usefulness of various features for the

Web People Search Task

• Architecture

Inputweb pages

Featurevectors

Clusters

Cosine similarity+

Single linkhierarchical agglomerative

clustering+

Minimum similaritythreshold



Evaluation

• F(α = 0.5) and similarity threshold 0.2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Tokens(T)

NamedEntities

(NE)

NEtargeted(NE-T)

Host (H) Host +Self (H-S)

Domain(D)

Domain +Self (D-S)

URL (U)

ECDL

Wikipedia

Census



Evaluation - Author Disambiguation

• Dataset– Manually-disambiguated dataset of 24 ambiguous

names in computer science domain– Each ambiguous name represented 2 unique

authors (k = 2) except for one where it represented 3– Each name is attributed to 30 citations on average– Proportion of largest class ranges from 50% to 97%

• Search engine– Google (http://www.google.com/)



Evaluation• Single link performs best

– Good for clustering citations from different publication pages together (some pages list only selected publications)

– Some authors have disparate research areas, not well represented by a centroid vector

• Resolving hostnames to IP addresses give best accuracy

0.827

0.726

0.8050.8070.798

0.811

0.836

0.734

0.812

0.660.680.700.720.740.760.780.800.820.840.86

Single link Complete link Group average

Hostname Domain IP address

Classification accuracyaveraged over all names



Discussion

Per-name accuracies using single link Per-name average number of URLsreturned per citation



Discussion• Apparent correlation between accuracy and average number

of URLs returned per citation– Author names with few URLs tend to fare poorly since results

are mainly aggregator web sites

What’s the cost?– Lots of queries needed– Web page downloads are expensive– Hence, slow

Can we speed this up?

Sure thing…



Query probing

• Consider some publication venues:–Joint Conference on Digital Libraries

–European Conference on Digital Libraries

–Digital Libraries

• Query probing– Use common n-gram “digital libraries” as query probe

– If we can obtain information on all three conferences, we save two queries



Adaptive queryingCombine two methods when needed

• Methods– Ms: stronger method but very slow (e.g. web page similarity)– Mw: weaker method but fast (e.g. host overlap)

• Aim– Accuracy close to Ms

– Significantly reduced running time than Ms

• Algorithm

– Execute Mw

– If heuristic suggests that Mw results are likely incorrect

Execute Ms



Entity Linkage - Conclusion

• Important problem with a rich history• New external methods poll contextual evidence for judgment• Need to combine methods to obtain best aspect of each



Talk Outline

• Linkage using the Web

>> Graph-based Update Summarization

– Introduction

– Timestamped Graphs

– Evaluation and Conclusions

“Now that all this data is linked, how do we process it?’’



Applications of Summarization

Decision Support

Doing Less Work



More seriously: an exciting challenge ......put a book on the scanner, turn the dial to ‘2 pages’, and read the result...

...download 1000 documents from the web, send them to the summarizer, and select the best ones by reading the summaries of the clusters...

...forward the Japanese email to the summarizer, select ‘1 par’, and skim the translated summary.

…get a weekly digest of new treatments and therapies for pressure ulcers

An update task



Simplifying summarization

Select important sentences verbatim from the input text to form a summary

– Input: A text document with k sentences

– Output: Top n (n << k) sentences with the highest numeric scores (each sentence in the input document is assigned a numeric score)

Extractive Summarization



Summarization

Heuristics for extractive summarization– Cue/stigma phrases– Sentence position (relative to document, section, paragraph)– Sentence length – TF×IDF, TF scores– Similarity (with title, context, query)

Machine learning to tune weights by supervised learning

Recently, graphical representations of text have shed new light on the summarization problem



Revisiting Social Networks: Prestige

One motivation was to model the problem as finding prestige of nodes in a social network

• PageRank: random walk

In summarization, lead to TextRank and LexRank

• Did we leave anything out of our representation for summarization?

Yes, the notion of an evolving network



Social networks change!Natural evolving networks (Dorogovtsev and Mendes, 2001)

– Citation networks: New papers can cite old ones, but the old network is static

– The Web: new pages are added with an old page connecting it to the web graph, old pages may update links



Talk Outline


• Graph-based Update Summarization– Introduction

>> Timestamped Graphs– Evaluation and Conclusion



Evolutionary models for summarization

Writers and readers often follow conventional rhetorical styles - articles are not written or read in an arbitrary way

Consider the evolution of texts using a very simplistic model

– Writers write from the first sentence onwards in a text

– Readers read from the first sentence onwards of a text

A simple model: sentences get added incrementally to the graph



Timestamped Graph Construction

These assumptions suggest us to iteratively add sentences into the graph in chronological order.

At each iteration, consider which edges to add to the graph.

– For single document: simple and straightforward: add 1st sentence, followed by the 2nd, and so forth, until the last sentence is added

– For multi-document: treat it as multiple instances of single documents, which evolve in parallel; i.e., add 1st sentences of all documents, followed by all 2nd sentences, and so forth

• NB: Doesn’t really model chronological ordering between articles, fix later




Model:

• Documents as columns – di = document i

• Sentences as rows–sj = jth sentence of document



Timestamped Graph Construction• A multi document example

doc1 doc2 doc3

sent1

sent2

sent3



An example TSG: DUC 2007 D0703A-A



These are just one instance of TSGs

Let’s generalize and formalize them

Def: A timestamped graph algorithm tsg(M) is a 9-tuple (d, e, u, f,σ, t, i, s, τ) that specifies a resultingalgorithm that takes as input the set of texts M andoutputs a graph G

Properties of nodes


Properties of edges

Input text transformation

function



Edge properties (d, e, u, f)• Edge Direction (d)

– Forward, backward, or undirected

• Edge Number (e)– number of edges to instantiate per timestep

• Edge Weight (u)– weighted or unweighted edges

• Inter-document factor (f)

– penalty factor for links between documents in multi-document sets.



Node properties (σ, t, i, s)• Vertex selection function σ(u, G)

– One strategy: among those nodes not yet connected to u in G, choose the one with highest similarity according to u– Similarity functions: Jaccard, cosine, concept links

(Ye et al.. 2005)

• Text unit type (t)– Most extractive algorithms use sentences as elementary units

• Node increment factor (i) – How many nodes get added at each timestep

• Skew degree (s)– Models how nodes in multi-document graphs are added– Skew degree = how many iterations to wait before adding the 1st sentence of the next document– Skip for today…



Timestamped Graph Construction• Representations

– We can model a number of different algorithms using this 9-tuple formalism:

(d, e, u, f, σ, t, i, s, τ)

– The given toy example:(f, 1, 0, 1, max-cosine-based, sentence, 1, 0, null)

– LexRank graphs:(u, N, 1, 1, cosine-based, sentence, Lmax, 0, null)

N = total number of sentences in the cluster; Lmax = the max document length

i.e., all sentences are added into the graph in one timestep, each connected to all others, and cosine scores are given to edge weights



System Overview• Sentence splitting

–Detect and mark sentence boundaries–Annotate each sentence with the doc ID and the sentence number –E.g., XIE19980304.0061: 4 March 1998 from Xinhua News; XIE19980304.0061-14: the 14th sentence of this document

• Graph construction–Construct TSG in this phase



System Overview• Sentence Ranking

– Apply topic-sensitive random walk on the graph to redistribute the weights of the nodes

• Sentence extraction

– Extract the top-ranked sentences

– Two different modified MMR re-rankers are used, depending on whether it is main or update task



Talk Outline


• Graph-based Update Summarization– Introduction

– Timestamped Graphs

>> Evaluation and Conclusion



Evaluation• Dataset: DUC 2005, 2006 and 2007. • Evaluation tool: ROUGE: n-gram based automatic evaluation• Each dataset contains 50 or 45 clusters, each cluster contains

a query and 25 documents

• Evaluate on some parameters– Do different e values affect the summarization process?

• e = 2 works best for DUC dataset

– How do topic-sensitivity and edge weighting perform in running PageRank?

• Applying both seems to have best effect

– How does skewing the graph affect the information flow in the graph?

• Skew of 1 works best, but need to try other possibilities



Holistic Evaluation in DUC 2007Extractive-based TSG system

Used modified maximal marginal relevance for update tasks

– Penalize links in previously read articles

– Extension of inter-document factor (f)

Cluster 1 Cluster 2 Cluster 3



Evaluation Results

Main task: 10th of 32 systems

Update task: 3rd of 24 systems

Conclusion• TSG formalism better tailored to deal with update / incremental text tasks• New method that may be competitive with current approaches

– Other top scoring systems may do sentence compression (abstractive), not just extraction



Graph-based Update Summary - Conclusion

Proposed a timestamped graph model for text understanding and summarization

– Adds sentences in an incremental fashion

Future work: – Freely skewed model– Empirical and theoretical properties of TSGs



Where do we go from here?

Thank you!http://wing.comp.nus.edu.sg/

Organizing data around entities, events • How people deal with data anyways• Understand objects and their inter/intra-relationship• Automation requires domain-expertise within a generic framework

“Collate all studies on SBP2 that new findings in the last year.”

“Oh, I meant the PROTEIN SBP2, not the gene.”

“What other proteins does SBP2 bind to?”

“Tell me more about the contradiction from previous results.”

“Which Miller did the study on SBP2 in 2002?”







Backup Slides – Entity Linkage

50 Minute talk total

7 Apr 2008, 10 – 11 AM



Social network analysis• Connected triple

• Random walk

• Maximum flow

• Clustering

x2

x1

x1

x2

x3

s t



Scalability Issues• Pairwise comparisons

– Requires O(n2) time– Major bottleneck

• Possible solutions– Blocking techniques– Avoiding pairwise

comparisons altogether

Input: d1, d2, …, dn

for i = 1 to n for j = (i + 1) to n compute sim(di, dj)



Cost-utility Framework

f1 f2

r1

f3 f4 f5

r2

r3

r4

r5

r6

c1 c2 c3 c4 c5 u1 u2 u3 u4 u5

cost ofacquiring fi

utility ofacquiring fifeature fi

known value

value that can be acquired



Record Matching

TITLE_MIN_LENTITLE_MAX_LEN

AUTHOR_MIN_LENAUTHOR_MAX_LEN

VENUE_MIN_LENVENUE_MAX_LEN

TITLE_SIMAUTHOR_SIMVENUE_SIM

MATCH/MISMATCH?

Header-reference pair (instance)

[1]Given information

[2]Information that canbe acquired at a cost

Training dataAssume all feature-valuesand their acquisition costsknown

Testing dataAssume [1] known, butfeature-values and theiracquisition costs in [2]unknown

CostsSet to MIN_LEN * MAX_LEN



Costs and Utilities•Costs

–Trained 3 models (using M5’), treat as regression•Utilities

–Trained 2^3 = 8 classifiers (each to predict match/mismatch using only known feature-values)–For a test instance with a missing feature-value F

Get confidence of appropriate classifier without F

Get expected confidence of appropriate classifier with F

Utility is difference between the two confidence scores

•Note–Similar to Saar-Tsechansky et al.



Results

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Normalized cost Recall Precision F-measure

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Normalized cost Recall Precision F-measure

Increasing proportion offeature-values acquired

Increasing proportion offeature-values acquired

Without cleaning of header records With manual cleaning of header records



Selected Bibliography•General and surveys

–Ivan P. Fellegi and Alan B. Sunter. A theory for record linkage. Journal of the American Statistical Association, 64(328):1183–1210, December 1969.–William E. Winkler and Yves Thibaudeau. An application of the Fellegi-Sunter Model of record linkage to the 1990 U.S. Decennial Census. Technical Report RR91/09, U.S. Bureau of the Census, 1991.–Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios. Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering (TKDE), 19(1):1–16, January 2007.–William E. Winkler. Overview of record linkage and current research directions. Technical Report RRS2006/02, U.S. Bureau of the Census, February 2006.–Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. Adaptive name matching in information integration. IEEE Intelligent Systems, 18(5):16–23, January/February 2003.–Min-Yen Kan and Yee Fan Tan. Record Matching in Digital Library Metadata. To appear in Communications of the ACM (CACM).



Selected Bibliography•String matching

–Robert A. Wagner and Michael J. Fischer. The string-to-string correction problem. Journal of the Association of Computing Machinery, 21(1):168–173, January 1974.–Saul B. Needleman and Christian D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 148(3):443–453, March 1970.–Temple F. Smith and Michael S. Waterman. Identification of common molecular subsequences. Journal of Molecular Biology, 147(1):195–197, March 1981.–Andrés Marzal and Enrique Vidal. Computation of normalized edit distance and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):926–932, September 1993.–Alvaro E. Monge and Charles Elkan. The field matching problem: Algorithms and applications. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 267–270, August 1996.–Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004.–Mikhail Bilenko and Raymond J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 39–48, August 2003.–Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005.–William. W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. A comparison of string distance metrics for name-matching tasks. In Information Integration on the Web (IIWeb), pages 73–78, August 2003.–Ariel S. Schwartz and Marti A. Hearst. A simple algorithm for identifying abbreviation definitions in biomedical text. In Pacific Symposium on Biocomputing (PSB), pages 451–462, January 2003.–Youngja Park and Roy J. Byrd. Hybrid text mining for finding abbreviations and their definitions. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 126–133, June 2001.–Jeffrey T. Chang , Hinrich Schütze, and Russ B. Altman. Creating an online dictionary of abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 9(6):612–620, November/December 2002.–Hiroko Ao and Toshihisa Takagi. ALICE: An algorithm to extract abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 12(5):576–586, September/October 2005.



Selected Bibliography•Direct classification or clustering, and blocking

–Hui Han, Hongyuan Zha, and C. Lee Giles. A model-based K-means algorithm for name disambiguation. In Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data, October 2003.–Hui Han, C. Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. Two supervised learning approaches for name disambiguation in author citations. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 296–305, June 2004.–Hui Han, Wei Xu, Hongyuan Zha, and C. Lee Giles. A hierarchical naive bayes mixture model for name disambiguation in author citations. In ACM Symposium on Applied Computing (SAC), pages 1065–1069, March 2005.–Hui Han, Hongyuan Zha, and C. Lee Giles. Name disambiguation in author citations using a K-way spectral clustering method. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 334–343, June 2005.–Dongwon Lee, Byung-Won On, Jaewoo Kang, and Sanghyun Park. Effective and scalable solutions for mixed and split citation problems in digital libraries. In ACM SIGMOD Workshop on Information Quality in Information Systems (IQIS), pages 69–76, June 2005.–Byung-Won On, Dongwon Lee, Jaewoo Kang, and Prasenjit Mitra. Comparative study of name disambiguation problem using a scalable blocking-based framework. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 344–353, June 2005.–Andrew McCallum, Kamal Nigam, and Lyle Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 169–178, August 2000.–Matthew Michelson and Craig A. Knoblock. Learning blocking schemes for record linkage. In National Conference on Artificial Intelligence (AAAI), July 2006.–Mikhail Bilenko, Beena Kamath, and Raymond J. Mooney. Adaptive Blocking: Learning to Scale Up Record Linkage and Clustering. In IEEE International Conference on Data Mining (ICDM), December 2006.



Selected Bibliography•Graphical models

–Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004.–John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning (ICML), pages 282–289, June/July 2001.–Andrew McCallum and Ben Wellner. Object consolidation by graph partitioning with a conditionally-trained distance metric. In ACM SIGKDD Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pages 19–24, August 2003.–Ben Wellner, Andrew McCallum, Fuchun Peng, and Michael Hay. An integrated, conditional model of information extraction and coreference with application to citation matching. In Conference on Uncertainty in Artificial Intelligence (UAI), pages 593–601, July 2004.–Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005.–Xin Dong, Alon Halevy, and Jayant Madhavan. Reference reconciliation in complex information spaces. In ACM SIGMOD International Conference on Management of Data, pages 85–96, June 2005.–Indrajit Bhattacharya and Lise Getoor. A latent dirichlet model for unsupervised entity resolution. In SIAM International Conference on Data Mining, pages 47–58, April 2006.



Selected Bibliography•Social network analysis

–H. A. Kautz, B. Selman, and M. A. Shah. The hidden web. AI Magazine, 18(2):27–36, 1997.–P. Mutschke. Mining networks and central entities in digital libraries. A graph theoretic approach applied to co-author networks. In Intelligent Data Analysis (IDA), pages 155–166, August 2003.–M. E. J. Newman. Who is the best connected scientist? A study of scientific coauthorship networks. In Complex Networks, pages 337–370, February 2004.–E. Otte and R. Rousseau. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6), December 2002.–T. Krichel and N. Bakkalbasi. A social network analysis of research collaboration in the economics community. In International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, May 2006.–R. Rousseau and M. Thelwall. Escher staircases on the world wide web. First Monday, 9(6), June 2004.–D. G. Feitelson. On identifying name equivalences in digital libraries. Information Research, 9(4), October 2004.–R. Bekkerman and A. McCallum. Disambiguating web appearances of people in a social network. In International conference on World Wide Web (WWW), pages 463–470, May 2005.–R. Holzer, B. Malin, and L. Sweeney. Email alias detection using social network analysis. In Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), August 2005.–B. Malin, E. Airoldi, and K. M. Carley. A network analysis model for disambiguation of names in lists. Computational and Mathematical Organization Theory, 11(2):119–139, July 2005.–G. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 150–160, August 2000.–P. K. Reddy and M. Kitsuregawa. An approach to build a cyber-community hierarchy. In SIAM ICDM Workshop on Web Analysis, April 2002.–Patrick Reuther. Personal name matching: New test collections and a social network based approach. Technical Report Mathematics/Computer Science 06-01, University of Trier, March 2006.–Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397-406, May 2006.



Selected Bibliography•Web-based methods

–Jamie P. Callan, Margie E. Connell, and Aiqun Du. Automatic discovery of language models for text databases. In ACM SIGMOD International Conference on Management of Data, pages 479–490, June 1999.–Jamie P. Callan and Margie E. Connell. Query-based sampling of text databases. ACM Transactions on Information Systems (TOIS), 19(2):97–130, April 2001.–Panagiotis G. Ipeirotis and Luis Gravano. Distributed search over the hidden-web: Hierarchical database sampling and selection. In International Conference on Very Large Databases (VLDB), pages 394–405, August 2002.–Luis Gravano, Panagiotis G. Ipeirotis, and Mehran Sahami. QProber: A system for automatic classification of hidden-web databases. ACM Transactions on Information Systems (TOIS), 21(1):1–41, January 2003.–Aron Culotta, Ron Bekkerman, and Andrew McCallum. Extracting social networks and contact information from email and the web. In Conference on Email and Anti-Spam (CEAS), July 2004.–Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In International conference on World Wide Web (WWW), pages 462–471, May 2004.–Philipp Cimiano, Günter Ladwig, and Steffen Staab. Gimme the context: Context-driven automatic semantic annotation with C-PANKOW. In International conference on World Wide Web (WWW), pages 332–341, May 2005.–Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397-406, May 2006.–Yee Fan Tan, Min-Yen Kan, and Dongwon Lee. Search engine driven author disambiguation. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), June 2006.–Ergin Elmacioglu, Min-Yen Kan, Dongwon Lee, and Yi Zhang. Googled name linkage. 2007.–Yee Fan Tan, Ergin Elmacioglu, Min-Yen Kan, and Dongwon Lee. Record Linkage of Short Forms to Long Forms: A Case Study of Publication Venues. 2007.–Min-Yen Kan. Web page classification without the web page. In International conference on World Wide Web (WWW), pages 262–263, May 2004.–Min-Yen Kan and Hoang Oanh Nguyen Thi. Fast webpage classification using url features. In International Conference on Information and Knowledge Management (CIKM), pages 325–326, October/November 2005.–Panagiotis G. Ipeirotis, Eugene Agichtein, Pranay Jain, and Luis Gravano. To search or to crawl? Towards a query optimizer for text-centric tasks. In ACM SIGMOD International Conference on Management of Data, pages 265–276, June 2006.

Backup Slides - Summarization

50 Minute talk total

7 Apr 2008, 10 – 11 AM



A Summarization Machine

Summary

MULTIDOCS

Extract Abstract

Indicative

Generic

Background

Query-oriented

Just the news

10%

50%

100%

Very Brief Brief

Long

Headline

Informative

DOC QUERY

Generate a summary given a text document



Summarization, defined• Definitions

Take a text document, extract content from it and present the most important content to the user in a condensed form and in a manner sensitive to the user’s or application’s needs

• Summarization requires::– understanding the meaning of a text document– generating fluent text summary

• Studies of human summarizers –Cremmins (65) & Endres-Niggemeyer (98) showed that professional summarizers used clues to pick summary content.



Skew Degree Examples

time(d1) < time(d2) < time(d3) < time(d4)

d1 d2 d3 d4 d1 d2 d3 d4

Skewed by 1 Skewed by 2 Freely skewed

d1 d2 d3 d4

Freely skewed = Only add a new document when it would be linked by some node using vertex function σ



Input text transformation function (τ)• Document Segmentation Function (τ)

– Problem observed in some clusters where some documents in a multi-document cluster are very long– Takes many timestamps to introduce all of the sentences, causing too many edges to be drawn

–Τ(G) segments long documents into several sub docs

• Solution is too hacked – hope to investigate more in current and future work

d5 d5bd5a



Evaluation on number of edges (e)Tried different e values • Optimal performance: e = 2• At e = 1, graph is too loosely connected, not suitable for PageRank

→ very low performance• At e = N, a LexRank system

N NN

e = 2e = 2



Evaluation (other edge parameters)• PageRank: generic vs topic-sensitive

• Edge weight (u): unweighted vs weighted

• Optimal performance: topic-sensitive PageRank and weighted edges

Topic-sensitive

Weighted edges

ROUGE-1 ROUGE-2

No No 0.39358 0.07690

Yes No 0.39443 0.07838

No Yes 0.39823 0.08072

Yes Yes 0.39845 0.08282



Evaluation on skew degree (s)• Different skew degrees: s = 0, 1 and 2

• Optimal performance: s = 1

• s = 2 introduces a delay interval that is too large

• Need to try freely skewed graphs

Skew degree ROUGE-1 ROUGE-2

0 0.36982 0.07580

1 0.37268 0.07682

2 0.36998 0.07489



Describing SummariesAspects of summarization (Sparck-Jones 97,

Hovy and Lin 99)• Input:

– Single-document vs. multi-document

• Purpose– Situation: embedded in larger system (MT, IR) or not? – Generic vs. query-oriented: author’s view or user’s interest?– Indicative vs. informative: categorization or understanding?– Background vs. just-the-news: does user have prior knowledge?

• Output– Extract vs. abstract: use text fragments or re-phrase content?



Differences for main and update task processing

Main task:

1. Construct a TSG for input cluster

2. Run topic-sensitive PageRank on the TSG

3. Apply first modified version of MMR to extract sentences

Update task:

• Cluster A:– Construct a TSG for cluster A– Run topic-sensitive PageRank on the TSG– Apply the second modified version of MMR to extract sentences

• Cluster B:– Construct a TSG for clusters A and B– Run topic-sensitive PageRank on the TSG; only retain sentences from B– Apply the second modified version of MMR to extract sentences

• Cluster C:– Construct a TSG for clusters A, B and C– Run topic-sensitive PageRank on the TSG; only retain sentences from C– Apply the second modified version of MMR to extract sentences



Sentence Ranking• Once a timestamped graph is built, we want to compute an prestige score for each node• PageRank: use an iterative method that allows the weights of the nodes to redistribute until stability is reached• Similarities as edges → weighted edges; query → topic-sensitive

Topic sensitive (Q)

portion

Standard random

walk term



Sentence Extraction – Main task• Original MMR: integrates a penalty of the maximal similarity of the candidate document and one selected document

• Ye et al. (2005) introduced a modified MMR: integrates a penalty of the total similarity of the candidate sentence and all selected sentences

• Score(s) = PageRank score of s; S = selected sentences• This is used in the main task

Penalty: All previous sentence similarity



Sentence Extraction – Update task•Update task assumes readers already read previous cluster(s)

– implies we should not select sentences that have redundant information with previous cluster(s)

• Propose a modified MMR for the update task: – consider the total similarity of the candidate sentence with all selected sentences and sentences in previously-read cluster(s)

• P contains some top-ranked sentences in previous cluster(s)

Previous cluster overlap



References• Günes Erkan and Dragomir R. Radev. 2004. LexRank: Graph-based centrality as salience in text summari-zation. Journal of Artificial Intelligence Research, (22).

• Rada Mihalcea and Paul Tarau. 2004. TextRank: Bring-ing order into texts. In Proceedings of EMNLP 2004.

• S.N. Dorogovtsev and J.F.F. Mendes. 2001. Evolution of networks. Submitted to Advances in Physics on 6th March 2001.

• Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Com-puter Networks and ISDN Systems, 30(1-7).

• Jon M. Kleinberg. 1999. Authoritative sources in a hy-perlinked environment. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms, 1999.

• Shiren Ye, Long Qiu, Tat-Seng Chua, and Min-Yen Kan. 2005. NUS at DUC 2005: Understanding docu-ments via concepts links. In Proceedings of DUC 2005.

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