CLiMB: Computational Linguistics for Metadata Building

CLiMB: Computational Linguistics

for Metadata Building

Center for Research on Information Access

Columbia University Libraries

January 21, 2003 CLiMB - Columbia University 2


Overall Goals

• 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


Computational Linguistic Techniques

• What techniques have we tried?

• How well have they worked?

• What else do we want to try?




– Goal: Identify high quality metadata terms

– Goal: Use metadata for finding images




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


Techniques We Have Tried

Unsupervised– Part of speech tagging– Noun phrase identification– Proper noun identification

Supervised (using existing resources)– Matching algorithms - proper names & variants– Back of book index analysis – Composite list of terms from authoritative lists


What about LSI?

• 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


Indexing for What Purpose

• Index = find important terms and phrases

• Index = characterize a document with a set of terms that occurs in the doc


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


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



– Goal: Identify high quality metadata terms

– Goal: Load metadata into image search database

– Goal: Use enriched metadata for finding images





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


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


Compile list of subject vocabulary

Find meaningful terms in texts

Collect terms from all sources.Identify and link AO-ID described in text.

Determine term relationships

Insert into existing metadata records.Mount in image search platform.

Process queries and evaluate

Segment relevant texts

Extract metadata


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


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










Extract metadata


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)• List of domain terms from authority sources

3) Common noun phrases in a generic domain vocabulary (chimney)• Statistical methods for identifying relevant terms


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


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


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


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

– Mary– Greene– Persian– Etc.



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


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• IBM – also commercial


Results: Proper NounsTool Precision Recall

AlembicWorkBench

97.50 91.20

LinkIT 68.94 98.81

LTChunk 68.13 63.48


Results: Proper Nouns

0102030405060708090

100

Wo

rkB

ench

Wo

rkB

ench

and

Lin

kIT

LT

Ch

un

k

Recall ofproper nounsin BosleyChapter 5

Precision


Results: NP Chunking

• Highlighted in purple:– The design process– The southwest adobe-stucco– July 1907



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









Extract metadata


What is Segmentation?

• Divide texts into cohesive chunks

• Needed for determining associational

context

• Needed to determine what terms are

related to an art object


Results: Segmentation

Project People, Frequency

0

2

4

6

8

10

12

1 4 7

10 13 16 19 22 25 28 31 34 37 40 43 46 49

Paragraph

Freq

uenc

y

Cole

Bolton

Thorsen

Pratt

Gamble

Blacker

Robinson

Ford

• 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


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


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


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….


Meronymy and Other Relations

The

California

House

Spinks House

porch terrace entry terrace

front door

Other Houses

front entry









Extract metadata


Progress – Project Name Matching

• Finding project names in Greene & Greene• Challenge: finding variations

– 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


Variants of The Culbertson House

• 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!


Zaoshen (Chinese deity)• 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))


Some Data to Illustrate

• Unaltered Project Names– 0 matches (both case sensitive and insensitive)

• Case Insensitive Project Name matching– 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


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


Summary: Research Tools Tested

• Part of Speech Taggers

• Noun Phrase Chunkers

• Merging techniques

• Proper Noun Finders

• Proper Name Variant Finder

• Segmenters









Extract metadata


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)









Extract metadata


Thank you!

Any questions?

www.columbia.edu/cu/cria/climb

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