CLiMB:
Computational Linguistics
for
Metadata Building
Center for Research on Information Access
Columbia University Libraries
January 21, 2003
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January 21, 2003
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Overall Goals
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Research: Development of richer retrieval through increased
numbers of descriptors
Research and Practice: Creation of enabling technologies for new
large digitization projects
Research and Practice: Expand capability for cross-collection
searching
Practice: Development of suite of CLiMB tools
Resources: Vocabulary list which can be used by other visual
resource professionals
The essence of CLiMB:
• Use scholars themselves as “catalogers” by utilizing scholarly
publications
• Enhance existing descriptive metadata
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Computational Linguistic Techniques
• What techniques have we tried?
• How well have they worked?
• What else do we want to try?
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Computational Linguistic Techniques
• What techniques have we tried?
– Goal: Identify high quality metadata terms
– Goal: Use metadata for finding images
• How well have they worked?
• What else do we want to try?
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Text about Images
The Blacker House is known for
its porte cochère and adjacent
terraces. Samuel Parker
Williams, an occasional Greene
collaborator, worked on the site,
particularly on the sandstone
boulder foundation for the
sleeping porch.
-- Based on Bosley
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Techniques We Have Tried
Supervised (using existing resources)
– Matching algorithms - proper names & variants
– Back of book index analysis
– Composite list of terms from authoritative lists
Unsupervised
– Part of speech tagging
– Noun phrase identification
– Proper noun identification
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What about LSI?
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Latent Semantic Indexing
Builds a representation of a document
Effective in information retrieval
Why not for CLiMB?
– LSI is useful for text query and document retrieval
– LSI, a statistical technique, removes phrasal info
– CLiMB needs high quality phrases
– May be useful in later stages
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Indexing for What Purpose
• Index = find important terms and phrases
• Index = characterize a document with a
set of terms that occurs in the doc
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Indexing for What Purpose
• Index = find important terms and phrases
– sleeping porch
– occasional collaborator
– sandstone boulder foundation
• Index = characterize a document with a
set of terms that occurs in the doc
– sleep*, porch, occas*, collaborat*, foundat*
– enables location of doc’s with similar profile
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Finding Similar Documents
• Linear Algebra Techniques
– Latent Semantic Indexing
• Singular Value Decomposition (SVD)
– Semidiscrete Decomposition
• Vector Space Models
– Term by Document matrices
– Term Weighting
– Polysemy and Synonymy
• Clustering Techniques
– K-means
– EM Clustering
– Wavelet
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Computational Linguistic Techniques
• What techniques have we tried?
– Goal: Identify high quality metadata terms
– Goal: Load metadata into image search database
– Goal: Use enriched metadata for finding images
• How well have they worked?
• What else do we want to try?
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Art Object Identification (AO-ID)
• Need Unique Identifiers
– Key of database records
• Varies from collection to collection
– Greene & Greene – Project Names
– Chinese Paper Gods – God Names
– South Asian Temples – Temple Names
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Text about Images
The Blacker House is known for its porte
cochère and adjacent terraces. Samuel
Parker Williams, an occasional Greene
collaborator, worked on the site,
particularly on the sandstone boulder
foundation for the sleeping porch.
-- Based on Bosley
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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Create Composite List of
Subject Terms
Philosophy: Use whatever resources exist
• Catalog records
– Robert R. Blacker house (Pasadena, Calif.)
– Greene, Charles Sumner
– Blacker, Robert R.
• Art and Architecture Thesaurus
– porte cochère
• Back of the book index
– Blacker house
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Progress – Composite List
• Greene & Greene
– Extracted back of the book indexes
– Direct matching of index terms to the text
• Terms found - highlighted in yellow
– David Gamble
– Pasadena
– Westmoreland Place
– furniture
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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Three Term Types and Approaches
1) Art Object ID names and other proper nouns
important to the domain (Charles Pratt)
•
Named Entity noun phrase finders, POS taggers
2) Common noun terms, semantically significant to
the domain (V-shaped plan)
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List of domain terms from authority sources
3) Common noun phrases in a generic domain
vocabulary (chimney)
•
Statistical methods for identifying relevant terms
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Part of Speech (POS) taggers
• Why use a part of speech tagger?
– To identify nouns, verbs and proper nouns
• The Blacker House is known for its porte cochère…
– <Determiner>The
– <Proper_Noun>
• <Singular_Proper_Noun>Blacker
• <Singular_Proper_Noun>House
– <Verb_Present>is
– <Verb_Past_Participle>known
– <Preposition>for
– <Possessive_Pronoun>its
– <Adjective>adjacent
– <Noun_Plural>terraces
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Part of Speech (POS) taggers
• Strength: An essential step allows the rest
of the system to work
• Weakness: The best POS taggers have
95% accuracy
– A typical 20-word sentence is likely to have a
mistake!
• But: some errors do not matter much
– E.g. sleeping porch
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What We Tried: POS Taggers
• Mitre Alembic WorkBench
– Freeware from Mitre corporation
– Strong for proper nouns
– Average for common nouns
• IBM’s Nominator
– Accurate for both
– Restrictive licensing
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Proper Nouns
• Alembic WorkBench Results
– 91.2% recall
• Misses The senior Pratt, Hall brothers
– 97.5% precision using Alembic
• Successfully finds William Issac Ott, University of California
• This is very good!
• Highlighted in light green
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Mary
Greene
Persian
Etc.
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Noun Phrase Chunking
[The [ Blacker House ] ] is known for
[ [its Porte Cochère] and [adjacent terraces] ].
[Samuel Parker Williams],
[an occasional Greene collaborator],
worked on [the site], particularly on
[the [ [sandstone boulder] foundation] ]
for [the [ sleeping porch ] ].
-- Based on Bosley
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NP Chunkers
• Columbia’s LinkIT
– Regular expression grammar over POS tags
– Improves WorkBench results through finding
simplex NPs
• LTChunk
– By LTG Group, University of Edinburgh
– Not as many NPs
• Arizona - commercialized
•January
IBM
–
also
commercial
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Results: Proper Nouns
Tool
Precision Recall
Alembic
WorkBench
LinkIT
97.50
91.20
68.94
98.81
LTChunk
68.13
63.48
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Results: Proper Nouns
January 21, 2003
LTChunk
WorkBench
and LinkIT
Recall of
proper nouns
in Bosley
Chapter 5
Precision
WorkBench
100
90
80
70
60
50
40
30
20
10
0
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Results: NP Chunking
• Highlighted in purple:
– The design process
– The southwest adobe-stucco
– July 1907
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Experiments with Algorithms
• TF/IDF and term frequency ratios
– Filter technical terms from frequent common nouns
– Term frequency ratio algorithm to improve accuracy
• Co-occurrence
– Useful terms may appear near other good ones
• Machine learning
– Use learning algorithms to discover complex
associational context
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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What is Segmentation?
• Divide texts into cohesive chunks
• Needed for determining associational
context
• Needed to determine what terms are
related to an art object
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Results: Segmentation
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Project People, Frequency
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10
Cole
Bolton
Thorsen
Pratt
Gamble
Blacker
Robinson
Ford
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6
4
2
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
0
1
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Use the frequency
that our terms
appear within a
document to
estimate where the
document is about
that term
This graph shows
where different
names are
mentioned in
Bosley on Greene
& Greene Ch. 5
Frequency
•
Paragraph
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What We’ve Tried: Segmenters
• Marti Hearst’s TextTiling
– Performs well for a general algorithm, but not
sufficient for this specialized task
– M. Hearst, ACL, 1993
• F. Choi’s C99 segmenter
– Performance comparable to TextTiling
– F. Y. Y. Choi, NAACL, 2000
• Frequency ratio approach outperformed
TextTiling
• In-house tool to be tested
– Kan & Klavans, WVLC-6, 1998, Segmenter
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Meronymy as “Part-Of”
• Why is this potentially useful?
– A method for identifying “hot” paragraphs
• Descriptive text contains “part of” relations
• Details that correlate to the whole
– Porch is a part of house
• An early hypothesis – in testing stages
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Meronymy for Cohesion
The Spinks house design is an elaboration of
the rectangular, large-gabled form of the
“California House” ….has … porches and
terraces. In front, an expanse of …lawn rises
nearly to the level of the entry terrace…. The
front door is approached obliquely in the
shaded recess of the terrace….
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Meronymy and Other Relations
The
California
House
Other Houses
Spinks House
porch
terrace
entry terrace
front entry
front door
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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Progress – Project Name Matching
• Finding project names in Greene & Greene
• Challenge: finding variations
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AO-ID Robert Roe Blacker House
RRB House
The house
1214 Fairlawn Terrace.
• Possible techniques to improve matching
– Developing a semi-automatic technique
– Use existing information to label text
– An iterative platform for manual intervention
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Variants of The Culbertson House
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Cordelia A. Culbertson house (Pasadena, Calif.)
Francis F. Prentiss house (Pasadena, Calif.)
Culbertson sisters house (Pasadena, Calif.)
Prentiss, Francis F.
Culbertson, Cordelia A.
Allen, Elizabeth S.
Allen, Mrs. Dudley P.
• House was purchased by Allen’s, who remarried and
became Prentiss!
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Zaoshen (Chinese deity)
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USE FOR: Dingfuzhenjun (Chinese deity)
USE FOR: Kitchen God (Chinese deity)
USE FOR: Simingzaojun (Chinese deity)
USE FOR: Simingzaoshen (Chinese deity)
USE FOR: Ssu-ming-tsao-chèun (Chinese deity)
USE FOR: Ssu-ming-tsao-shen (Chinese deity)
USE FOR: Ting-fu-chen-chèun (Chinese deity)
USE FOR: Tsao-chèun (Chinese deity)
USE FOR: Tsao-shen (Chinese deity)
USE FOR: Tsao-wang (Chinese deity)
USE FOR: Tsao-wang-yeh (Chinese deity)
USE FOR: Zaojun (Chinese deity)
USE FOR: Zaowang (Chinese deity)
REFERENCE: Encyc. Britannicab(Tsao Shen, pinyin Zao Shen, in Chinese
mythology, the god of the kitchen (god of the hearth), who is believed to
report to the celestial gods on family conduct and have it within his power to
bestow poverty or riches on individual families; has also been confused with
Ho Shen (god of fire) and Tsao Chèun (Furnace Prince))
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Some Data to Illustrate
• Unaltered Project Names
– 0 matches (both case sensitive and insensitive)
• Case Insensitive Project Name matching
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–
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4 matches
{Theodore Irwin house} occurs 1 time
{California Institute of Technology} occurs 1 time
{William R. Thorsen house} occurs 1 time
{William T. Bolton house} occurs 1 time
• At least double in the chapter
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A Future Solution
• Bootstrapping algorithm
– Seed terms hand labelled
– Terms mapped into multi-dimensional feature space
– Other terms that are close to the seed terms are
added to the set
• Features:
– Window size
– Headedness
– Modifier similar to that of a seed term
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Summary: Research Tools Tested
• Part of Speech Taggers
• Noun Phrase Chunkers
• Merging techniques
• Proper Noun Finders
• Proper Name Variant Finder
• Segmenters
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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Future: Determine relationships
• The Blacker House related to Greene
– The Greenes built the house.
• Porte Cochère is related to Blacker House
– because they are directly a part of the house.
• William Issac Ott is related to
– Blacker House (on which he worked)
– Greene (with whom he worked).
• Detecting these semantic relationships statistically is a
challenge for our next steps:
– Co-occurrence
– Use of subject headings
– Meronymy and other relations (WordNet)
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Compile list of
subject vocabulary
Find meaningful
terms in texts
Segment relevant
texts
Collect terms from all sources.
Identify and link AO-ID described in text.
Determine term relationships
Extract metadata
Insert into existing metadata records.
Mount in image search platform.
Process queries and evaluate
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Thank you!
Any questions?
www.columbia.edu/cu/cria/climb
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