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Kernel Canonical Correlation Analysis. Cross-language information retrieval. Blaz Fortuna JSI, Slovenija. Input. Two different views of the same data:. Text documents written in different languages Images with attached text …. Goal.

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kernel canonical correlation analysis

Kernel Canonical Correlation Analysis

Cross-language information retrieval

Blaz Fortuna

JSI, Slovenija


Two different views of the same data:

  • Text documents written in different languages
  • Images with attached text

Find pairs of features from both views with highest correlations

Example: words that co-appear in document and its translation

Auto, Fahrzeug, …

car, vehicle, …

Fleisch, Hahnchen, Rindfleisch, Schweinerne, …

meat, chicken, beef, pork, …

theory behind cca
Theory behind CCA
  • Documents are presented with pairs of vectors – one for each view
  • Result of CCA are basis vectors for each view such that the correlation between the projections of the variables onto these basis vectors are mutually maximized
kernelisation of cca
Kernelisation of CCA
  • Method can be rewritten so feature vectors only appear inside inner-product
  • We can use Kernel for calculating inner-product
  • Input documents don not need to be vectors (eg. text documents together with string kernel)
cross language text mining
Cross-Language Text Mining
  • KCCA constructs language independent representation for text documents
  • Good part: documents from different languages can be compared using this representation
  • Bad part: paired dataset is needed for training (can be avoided using machine translation tools)
kcca and lsi
  • LSI discovers statistically most significant co-occurrences of terms in documents
    • When word appears in a document, what other words usually also appear?
  • KCCA matches terms from the first language with terms from the second based on co-occurrences
    • When word appears in a document, does it also appear in its translation?
text document retrieval
Text document retrieval
  • Query databases with multilingual documents
  • Documents from database and query are transformed into language independent representation
  • Nearest neighbour
  • 36th Canadian Parliament proceedings corpus
  • Part of documents used for training
  • For testing 5 most relevant keywords were extracted from a document and used as queries
  • English query, French documents

retrieval accuracy (top-ranked/top-ten-ranked) [%]

text categorization
Text categorization
  • Categorize multilingual documents
  • All documents are transformed into language independent representation
  • Classifier is trained on transformed labelled documents
  • NTCIR-3 patent retrieval test collection
  • Japanese – English
  • SVM trained on English documents
  • Tested both on the Japanese and English

Average precision [%]

image text retrieval
Image-Text Retrieval
  • Retrieval of images based on a text query
  • No labels associated with images
  • Paired dataset:
    • Image retrieved from internet
    • Text on web page where image appeared
  • Querying database with images with text queries
  • Images were split into three clusters
  • 10 or 30 images that best match query are retrieved
  • In first test success is when images are of same label
  • In second test success is when images that actually matched query is retrieved

Images retrieved for the text query:

”height: 6-11 weight: 235 lbs position: forward born: september 18, 1968, split, croatia college: none”

”at phoenix sky harbor on july 6, 1997. 757-2s7, n907wa phoenix suns taxis past n902aw teamwork america west america west 757-2s7, n907wa phoenix suns taxis past n901aw arizona at phoenix sky harbor on july 6, 1997.”
feature work
Feature work
  • Use of machine translation for making paired dataset
  • Experiments with SVEZ-IJS English-Slovene ACQUIS Corpus
  • Sparse version of KCCA