A Study of Learning a Merge Model for Multilingual Information Retrieval

Intelligent Database Systems Lab

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

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A Study of Learning a Merge Model for Multilingual Information Retrieval

Presenter : Cheng-Hui Chen 　Author : Ming-Feng Tsai, Yu-Ting Wang, Hsin-Hsi Chen

SIGIR 2008


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Outlines Motivation Objectives Methodology Experiments Conclusions Comments


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Motivation· Multilingual information retrieval (MLIR) that result

list usually includes more irrelevant words.· Traditional merging methods for MLIR that assumption

relevant documents are homogeneously distributed over monolingual result lists.


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I. M.Objectives· The various translation and retrieval qualities in

different collections that to merge a unique result list.· To proposes merge method doesn’t assumption

relevant documents are homogeneously distributed over monolingual result lists.

· The enhancement merge model quality.

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I. M.Methodology· Traditional MLIR Framework.

─ Raw-score─ Round-robin─ Normalized-by-top1─ Normalized-by-top k

· The Proposes a learning method.

─ FRank

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I. M.MLIR merge process

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Feature Set1. Query levels2. Document levels3. Translation levels

The Construction of a Merge Model1. FRank ranking

algorithm2. BM25


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I. M.Feature set· Query levels

─ The manually classify the terms within a query into several pre-defined categories. Location/country names (Loc) Organization names (Org) Event names (EN) Technical terms (TT)

· Document levels─ The extracted document length (Dlength) and title length

(Tlength).

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I. M.Feature set· Translation levels

─ The size of a bilingual dictionary used for various language (i.e., DictSize).

─ The average number of translation equivalents within a query (i.e., AvgTAD). If a query has two query terms both with three translation

equivalents.· AvgTAD of the query is (3 + 3)/2 = 3.

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AvgTAD DicSize

(4+2)/2=2 3

中文(Translation

QT)(Order) (Park) 訂單公園順序停車命令隊形

中文翻譯數目查詢詞的數目

EN

Loc

EN

斗六

食べる

Order 、 Park

英 ->中Loc


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I. M.The Construction of Merge model· The FRank’s generalized additive model, a merge

model can be represented as :

─ mt(x) is a weak learner─ αt is the learned weight ─ t is the number of selected weak learners

· The combine with a retreval model (bm25) by using linear combination .

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I. M.Experiments· Data set

─ The Details of Experimental Collections

─ The Percentage of Retrieved Documents

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I. M.Experiments· Mean Average Precision (MAP)

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I. M.Experiments· The Experimental Results of Our Method

using Different Combination Coefficient λ.

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I. M.Experiments· Feature Analysis

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I. M.Conclusions· The proposed merge model can significantly

improve merging quality.· The merge model indicates the key factors are

the number of translatable terms and compound words.

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I. M.Conclusions· The future work

─ Use other learning-based ranking algorithms. Such as RankSVM and RankNet.

─ Extract more representative features to construct a merge model. Such as linguistic features.

─ Expect to discover more relations within query terms. Such as query term association and substitution.

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Comments· Advantage

─ Improve merging quality.· Drawback· Application

─ Multilingual Information retrieval.