Download Database Application Design - CLAIR

November 30, 2000
Language and Information
Handout #5
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Course Information
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Instructor: Dragomir R. Radev ([email protected])
Office: 305A, West Hall
Phone: (734) 615-5225
Office hours: TTh 3-4
Course page: http://www.si.umich.edu/~radev/760
Class meets on Thursdays, 5-8 PM in 311 West Hall
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Clustering (Cont’d)
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Using similarity in visualization
• Dendrograms (see Figure 14.1 of M&S,
page 496)
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Types of clustering
• Hierarchical: agglomerative, divisive
• Soft & hard
• Similarity functions:
– Single link: most similar members
– Complete link: least similar members
– Group-average: average similarity
• Applications:
– improving language models
– etc.
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HITS-type algorithms
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Hyperlinks and resource
communities
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Jon Kleinberg (Almaden, Cornell)
authoritative sources
www.harvard.edu --> Harvard
conferring authority via links
global properties of authority pages
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Hubs and authorities
hubs
authorities
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unrelated pages
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Authorities
• Java: www.gamelan.com, java.sun.com,
sunsite.unc.edu/javafaq/javafaq.html
• Censorship: www.eff.org,
www.eff.org/blueribbon.html, www.aclu.org
• Search engines: www.yahoo.com,
www.excite.com, www.mckinley.com,
www.lycos.com
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Related pages
• www.honda.com: www.ford.com,
www.toyota.com, www.yahoo.com
• www.nyse.com: www.amex.com,
www.liffe.com, update.wsj.com
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Collocations
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Collocations
• Idioms
• Free word combinations
• Know a word by the company that it keeps
(Firth)
• Common use
• No general syntactic or semantic rules
• Important for non-native speakers
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Examples
Idioms
Collocations
Free-word combinations
To kick the bucket
Dead end
To catch up
To trade actively
Table of contents
Orthogonal projection
To take the bus
The end of the road
To buy a house
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Uses
• Disambiguation (e.g, “bank”/”loan”,”river”)
• Translation
• Generation
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Properties
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Arbitrariness
Language- and dialect-specific
Common in technical language
Recurrent in context
(see Smadja 83)
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Arbitrariness
• Make an effort vs. *make an exertion
• Running commentary vs. *running
discussion
• Commit treason vs. *commit treachery
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Cross-lingual properties
• Régler la circulation = direct traffic
• Russian, German, Serbo-Croatian: direct
translation is used
• AE: set the table, make a decision
• BE: lay the table, take a decision
• “semer le désarroi” - “to sow disarray” - “to
wreak havoc”
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Types of collocations
• Grammatical: come to, put on; afraid that,
fond of, by accident, witness to
• Semantic (only certain synonyms)
• Flexible: find/discover/notice by chance
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Base/collocator pairs
Base
Collocator
Example
Noun
Noun
Verb
Adjective
Verb
verb
adjective
adverb
adverb
preposition
Set the table
Warm greetings
Struggle desperately
Sound asleep
Put on
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Extracting collocations
• Mutual information
I (x;y) = log2
P(x,y)
P(x)P(y)
• What if I(x;y) = 0?
• What if I(x;y) < 0?
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Yule’s coefficient
A - frequency of lemma pairs involving both
Li and Lj
B - frequency of pairs involving Li only
C - frequency of pairs involving Lk only
D - frequency of pairs involving neither
YUL =
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AD - BC
AD + BC
-1  YUL  1
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Specific mutual information
• Used in extracting bilingual collocations
p (e,f)
I (e,f) =
p(e) p(f)
• p(e,f) - probability of finding both e and f in
aligned sentences
• p(e), p(f) - probabilities of finding the word in one
of the languages
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Example from the Hansard
corpus (Brown, Lai, and Mercer)
French word Mutual information
sein
5.63
bureau
5.63
trudeau
5.34
premier
5.25
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résidence
intention
5.12
4.57
no
session
4.53
4.34
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Flexible and rigid collocations
• Example (from Smadja): “free” and “trade”
Total p-5 p-4 p-3 p-2
8031
7
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13
5
p-1
7918
p+1 p+2 p+3 p+4 p+5
0
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26
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Xtract (Smadja)
• The Dow Jones Industrial Average
• The NYSE’s composite index of all its
listed common stocks fell *NUMBER* to
*NUMBER*
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Translating Collocations
• Brush up a lesson, repasser une leçon
• Bring about/осуществлять
• Hansards: late spring: fin du printemps,
Atlantic Canada Opportunities Agency,
Agence de promotion économique du
Canada atlantique
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The eSseNSe system
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Offline processing
Online processing
user query
cached documents
cluster 1
cluster 2
cluster 3
…
…
cluster n
hitlist
…
summary 1
summary 2
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summary 3
summary n
summary
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Sample summary
Sentence
Score
The idea behind data mining then is the non-trivial process of identifying valid novel potentially useful and ultimately
understandable patterns in data 18 2 The term knowledge discovery in databases KDD was formalized in 1989 in reference to the
general concept of being broad and 'high level' in the pursuit of seeking knowledge from data
494.92
The term data mining is then this high-level application techniques / tools used to present and analyze data for decision makers
509.11
This term data mining has been used by statisticians data analyst and the MIS management information systems community whereas
KDD has been mostly used by artificial intelligence and machine learning researchers
487.92
These are : -the untapped value in large databases consolidation of database records tending towards a single customer view concept
of an information or data warehouse from the consolidation of databases dramatic drop in the cost/performance ratio of hardware
systems - for data storage and processing
576.60
Intense competition in an increasing saturated marketplace the ability to custom manufacture market and advertise to small market
segments and individuals 4 and the market for data mining products is estimated at about 500 million in early 1994 12 Data mining
technologies are characterized by intensive computations on large volumes of data
486.92
Data mining versus traditional database queries Traditional database queries contrasts with data mining since these are typified by
the simple question such as what were the sales of orange juice in January 1995 for the Boston area
520.53
Data mining on the other hand through the use of specific algorithms or search engines attempts to source out discernable patterns
and trends in the data and infers rules from these patterns
500.80
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Cluster
ID
Number
of URLs
Centroi d words
00085
63
00044
167
00086
115
retrieval in formation university ir text research systems science semantic
evaluation document pp library
00657
135
vol pp no retrieval proceedings conference user ir informat ion query
00766
6
00127
4
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informat ion retrieval systems university workshop papers research library science
submission chair text edu conference computer applications language processing
data libraries
web internet site design hosting search online sites commerce page business meta
your information content server marketing you electronic pages
user interface users ariadne hypertext search computer interfaces we designer
system laurel informat ion nelson knowledge collaboration representation
interaction process systems
neural systems computational data evolutionary intelligent networks learning
artificial knowledge intelligence
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mining (84.54), data (64.13), knowledge (14.25), discovery (11.98), advertised (11.20), databases
(9.69), information (6.98), research (6.96), analysis (6.95), text (6.05), patterns (5.30), algorithms
(4.39).
Text Min ing is a new and excit ing research area that tries to solve the informat ion overload
problem by using techniques from data mining mach ine learning info rmation retrieval naturallanguage understanding case-based reasoning statistics and knowledge management to help
people gain insight into large quantities of semi-structured or unstructured text
<BR> Current Projects - A ims The Data M ining Program has two projects: DM ITL - Data
Mining in the Large Conducting practical case studies for clients involving the analysis of
large and co mplex data sets ParAlg – Parallel Algorith ms The main co mputing facility for
these projects consists of a secure mu ltiprocessor Sun E4000
The DMITL project aims to develop knowledge and techniques relevant to the data mining of
large and co mplex datasets using high performance co mputers
Megaputer develops software and solutions for data mining text analysis and knowledge
discovery in databases
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183.73
156.56
163.78
188.96
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Cross-language information
access
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CE
SE
QE
DE
SE
English
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CE
SE
QC
QE
DE
SE
English
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Chinese
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CE
SE
SC
CC
QC
QE
DE
SE
English
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SC
DC
Chinese
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CE
SE
SC
CC
QC
QE
DE
SE
English
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SC
DC
Chinese
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CE
SE
SC
CC
QC
QE
DE
SE
English
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SC
DC
Chinese
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CE
SE
SC
CC
QC
QE
DE
SE
English
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SC->E
DC
Chinese
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Objectives
• Produce summaries using multiple algorithms
• evaluate summarization and translation separately
• intrinsic and extrinsic language-independent
evaluation metrics
• establish correlation between evaluation metrics
• build parallel C-E doc+summary 9K docs (Hong
Kong news)
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Participants
• Full time
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K.-L. Kwok, Queens College
Dragomir Radev, U. Michigan
Wai Lam, Chinese University of HK
Simone Teufel, Columbia
• “Consultants”
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Chin-Yew Lin, ISI
Tomek Strzalkowski, Albany
Jade Goldstein, CMU
Jian-Yun Nie, U. Montréal
• Supporters
– TIDES roadmap group: Ed Hovy, Daniel Marcu, Kathy McKeown
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Techniques and parameters
• Summarization:
– position, TF*IDF, centroids, largest common
subsequence, keywords
• Evaluation:
– intrinsic: percent agreement, relative utility,
precision/recall
– extrinsic: document rank, question answering
• Length of documents/summaries
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The parallel corpus
• English and Chinese (Hong Kong News)
• Already there:
– 9000 documents and their translations
– list of 300 queries in English and their translations
• We will create before the workshop:
– document relevance judgements
• 50 queries, 5 hrs/query, $10/hr -> $2,500
– sentence relevance judgements
• 4 doc/hr, need 4000 rel. judgements -> $10,000
– optional: manual abstracts
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Creating the judgements
• For each query
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submit to IR engine
discard unless it has 5-20 hits
get exhaustive document relevance judgements
consider top 100 documents
• get sentence relevance judgements for
– all relevant judgements
– top 50 documents (including irrelevant ones!)
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Experiments
• Experiment 1: (Validation)
Compare preservation of ranking with other measures: judgement overlap,
relative utility
• Experiment 2 & Experiment 3:
– use with preservation of ranking
– only possible due to new, parallel experimental design
– factor out effects of
• query translation
• summarization
• monolingual IR
• Baseline:
– leading sentence summaries vs. documents
– other summarization methods vs. documents
– (ideal: manual summaries vs. documents)
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Experiments
• Monolingual experiments
– Effect of summarization
• English Query -> English Doc (ranks)
• English Query-> English Summary (ranks)
• Chinese Query -> Chinese Doc (ranks)
• Chinese Query -> Chinese Summary (ranks)
– Baseline:
• leading sentence summary vs. document
• ideal: manual summary vs. document
– Effect of language on IR
• English Query -> English Doc
• Chinese Query-> Chinese Doc
• Experiment 2: crosslingual
– Effect of query translation
• English Query -> English Doc
• English Query -> Chinese Query -> Chinese Doc
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Timeline
• Pre-workshop: build corpus
• Sentence segmenter, Chinese tokenizer, machine
translation, IR system, eSseNSe summarizer
• Workshop: system integration, build toolkit,
summarization, evaluation, correlation, system
refinement, final evaluation
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Workshop
W1
W2
W3
W4
W5
W6
Set up experimental testbed
Evaluation plan laid out
Selection of training/test sub-corpora
Alpha version of CLIA system tested on a small number of queries
Baseline experiment
Run experiment one
Run experiment two
Compute query translation quality
Run experiment three
Feedback from first three experiments
System improvements
Improved CLIA system ready
Evaluation using unseen test data
Draft of final report
Additional experiments
Wrap-up
Final version of CLIA system released
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together
otherwise
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together otherwise
• Scientific merit: much-needed evaluation metrics,
techniques for multi-document, multilingual
summarization, incorporate utility, redundancy,
subsumption
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of Michigan
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together otherwise
• Scientific merit: much-needed evaluation metrics, techniques
for multi-document, multilingual summarization, incorporate
utility, redundancy, subsumption
• Feasibility: uses existing work, specific plan for new work
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of Michigan
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together otherwise
• Scientific merit: much-needed evaluation metrics, techniques
for multi-document, multilingual summarization, incorporate
utility, redundancy, subsumption
• Feasibility: uses existing work, specific plan for new work
• Community building: corpora, evaluation techniques, and
software (CLIR, evaluation, and summarization), builds
on prior evaluations (TDT, TREC, SUMMAC, DUC)
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of Michigan
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together otherwise
• Scientific merit: much-needed evaluation metrics, techniques
for multi-document, multilingual summarization, incorporate
utility, redundancy, subsumption
• Feasibility: uses existing work, specific plan for new work
• Community building: corpora, evaluation techniques, and
software (CLIR, evaluation, and summarization), builds on
prior evaluations (TDT, TREC, SUMMAC, DUC)
• Funder interest: multilingual systems, large amounts of
data
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of Michigan
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Merit criteria
• Novelty: never done before, integration of CLIR and
summarization
• Collaboration: participants wouldn’t work together otherwise
• Scientific merit: much-needed evaluation metrics, techniques
for multi-document, multilingual summarization, incorporate
utility, redundancy, subsumption
• Feasibility: uses existing work, specific plan for new work
• Community building: corpora, evaluation techniques, and
software (CLIR, evaluation, and summarization), builds on
prior evaluations (TDT, TREC, SUMMAC, DUC)
• Funder interest: multilingual systems, large amounts of data
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What was dropped
• Interactive clustering of documents
• Evaluation of the quality of translated
summaries
• Document translation
• Effects of document genre, length
• Evolving summaries
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More…
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What we didn’t talk about
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Hidden Markov models
Part of speech tagging
Probabilistic parsing
Information retrieval
Text classification
etc.
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THE END ?
http://perun.si.umich.edu/~radev/760/job/
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