Kernel Canonical Kernel Canonical Correlation Correlation AnalysisAnalysis

Blaz FortunaJSI, Slovenija

Cross-language Cross-language information retrievalinformation retrieval

Input

Two different views of the same data: Text documents

written in different languages

Images with attached text

…

Goal

Find pairs of features from both views with highest correlations

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

car, vehicle, …

Auto, Fahrzeug, …

meat, chicken, beef, pork, …

Fleisch, Hahnchen, Rindfleisch, Schweinerne, …

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 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 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 Query databases with multilingual

documents DocumentsDocuments from database and

queryquery are transformed into language independent representation

Nearest neighbour

Experiments 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

100 200 300 400 500

LSI 30/67 38/75 42/79 45/81 49/84

KCCA 68/94 75/96 78/97 79/98 81/98

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

Text categorization Categorize multilingual documents All documents are transformed into

language independent representation

Classifier is trained on transformed labelled documents

Experiments NTCIR-3 patent retrieval test collection Japanese – English SVM trained on English documents Tested both on the Japanese and English

50 100 150 Full

Eng-train

87.6 93.9 95.8 97.1

Eng-test 85.1 87.4 87.0 87.9

Jp-train 87.4 92.9 95.4 96.8

Jp-test 77.2 77.7 77.3 78.4Average precision [%]

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

Experiments 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

10 30 10 30

30 dim 85% 91% 17% 60%

150 dim 83% 91% 32% 69%

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 Use of machine translation for

making paired dataset Experiments with SVEZ-IJS English-

Slovene ACQUIS Corpus Sparse version of KCCA