Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Advisor ： Dr. Hsu

Presenter ： Chien Shing Chen

Author: Wei-Hao Lin and Hsin-His Chen

Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

Proceedings of 2003 Workshop of Cross Language Evaluation Forum,Norway, August, 2003.


N.Y.U.S.T.

I. M.Outline

Motivation Objective Introduction Backward Transliteration Query Translation Experimental Result Conclusions Personal Opinion


N.Y.U.S.T.

I. M.Motivation

How to retrieve multimedia data precisely a important research issue.

People with no strong language skills can easily understand the relevance of the retrieved images.

IR systems must handle proper nouns transliteration approximately to achieve better performance.


N.Y.U.S.T.

I. M.Objective

adopt text-based approach to deal with the Chinese-English cross-language image retrieval problem


N.Y.U.S.T.

I. M.Introduction

Chinese

Phoneme

English

IPA

Similarity score +MI

MI

Phoneme

IPA

Input

F-2-H-F

F-2-H-F: First –two-highest-frequency

MI: Mutual Information

1

2

3

4 Similarity score +F2HF


N.Y.U.S.T.

I. M.Similarity Measurement-Dynamic

Dynamic programming to trade off :alignment

similarity scoring matrix M

OPTIMALS1 (j h u g oU)

S2 (v k uo)


N.Y.U.S.T.

I. M.Candidate Filter

A transliterated word and its original word contain the same phonemes, and the order of the phonemes are the same.

After retrieving, the top rank of candidate words as the appropriate candidates of the transliterated word.


N.Y.U.S.T.

I. M.Candidate Filter

x: Chinese phoneme

y: English phoneme


N.Y.U.S.T.

I. M.Query Translation

We adopted the following two methods to select appropriate translations:

CO modeladopt MI to measure the co-occurrence strength between words

First-two-highest-frequencyhighest occurrence frequency in the English image captions were considered as the target language query terms


N.Y.U.S.T.

I. M.Query Translation

150 distinct Chinese query terms

Total 16 of 150 query terms is unknown word.

The terms contain 7 person names and 5 location names, and were translated by foreign names.


N.Y.U.S.T.

I. M.Experiments


N.Y.U.S.T.

I. M.Conclusions

Text-based image retrieval and query translation were adopted in the experiments.


N.Y.U.S.T.

I. M.Personal Opinion

DrawbackThe corpus is not clear.

Applicationapply to text-based IR

Future Workidentify unknown word is still a challenge

Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

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