CLiMB: Computational Linguistics for Metadata Building

1. Center for Research on Information Access Columbia University Libraries CLiMB: Computational Linguistics for Metadata Building CLiMB - Columbia University

2. CLiMB - Columbia University

3. 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 CLiMB - Columbia University

4. Computational Linguistic Techniques • What techniques have we tried? • How well have they worked? • What else do we want to try? CLiMB - Columbia University

5. 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? CLiMB - Columbia University

6. 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 CLiMB - Columbia University

7. 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 CLiMB - Columbia University

8. 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 CLiMB - Columbia University

9. Indexing for What Purpose • Index = find important terms and phrases • Index = characterize a document with a set of terms that occurs in the doc CLiMB - Columbia University

10. 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 CLiMB - Columbia University

11. 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 CLiMB - Columbia University

12. 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? CLiMB - Columbia University

13. 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 CLiMB - Columbia University

14. 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 CLiMB - Columbia University

15. 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 CLiMB - Columbia University

16. 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 CLiMB - Columbia University

17. 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 CLiMB - Columbia University

18. CLiMB - Columbia University

19. 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 CLiMB - Columbia University

20. 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 CLiMB - Columbia University

21. 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 CLiMB - Columbia University

22. 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 CLiMB - Columbia University

23. 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 CLiMB - Columbia University

24. 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. CLiMB - Columbia University

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26. 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 CLiMB - Columbia University

27. 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 CLiMB - Columbia University

28. Results: Proper Nouns CLiMB - Columbia University

29. Results: Proper Nouns CLiMB - Columbia University

30. Results: NP Chunking • Highlighted in purple: • The design process • The southwest adobe-stucco • July 1907 CLiMB - Columbia University

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32. 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 CLiMB - Columbia University

33. 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 CLiMB - Columbia University

34. What is Segmentation? • Divide texts into cohesive chunks • Needed for determining associational context • Needed to determine what terms are related to an art object CLiMB - Columbia University

35. Results: Segmentation • 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 CLiMB - Columbia University

36. 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 CLiMB - Columbia University

37. 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 CLiMB - Columbia University

38. Meronymy for Cohesion TheSpinks housedesign is an elaboration ofthe rectangular, large-gabled form of the “California House” ….has …porchesandterraces. In front, an expanse of …lawnrises nearly to the level of theentry terrace…. Thefront dooris approached obliquely in the shaded recess of the terrace…. CLiMB - Columbia University

39. Meronymy and Other Relations The California House Other Houses Spinks House entry terrace front entry terrace porch front door CLiMB - Columbia University

40. 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 CLiMB - Columbia University

41. 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 CLiMB - Columbia University

42. 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! CLiMB - Columbia University

43. 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)) CLiMB - Columbia University

44. 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 CLiMB - Columbia University

45. 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 CLiMB - Columbia University

46. Summary: Research Tools Tested • Part of Speech Taggers • Noun Phrase Chunkers • Merging techniques • Proper Noun Finders • Proper Name Variant Finder • Segmenters CLiMB - Columbia University

47. 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 CLiMB - Columbia University

48. 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) CLiMB - Columbia University

49. 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 CLiMB - Columbia University

50. Thank you! Any questions? www.columbia.edu/cu/cria/climb CLiMB - Columbia University