Building Structured Query in Target Language for Vietnamese – English Cross Language Information Retrieval Systems

Giang, Lam Tung; Hung, Vo Trung; Pháp, Huỳnh Công

doi:10.17577/ijertv4is040317

Cited by 3 publications

(9 citation statements)

References 10 publications

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“…The training process consists of five steps as follows: - [20], but retain the evaluation of non-linear functions. The function is binary tree, with inner vertices being operators, leaf vertices are constants or variables.…”

Section: Modelling Application Of Genetic Programmingmentioning

confidence: 99%

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New Re-Ranking Approach in Merging Search Results

2019

IJCAI

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When merging query results from various information sources or from different search engines, popular methods based on available documents scores or on order ranks in returned lists, its can ensure fast response, but results are often inconsistent. Another approach is downloading contents of top documents for re-indexing and re-ranking to create final ranked result list. This method guarantees better quality but is resource-consuming. In this paper, we compare two methods of merging search results: a) applying formulas to re-evaluate document based on different combinations of returned order ranks, documents titles and snippets; b) Top-Down Re-ranking algorithm (TDR) gradually downloads, calculates scores and adds top documents from each source into the final list. We propose also a new way to re-rank search results based on genetic programming and re-ranking learning. Experimental result shows that the proposed method is better than traditional methods in terms of both quality and time. Povzetek: V prispevkih sta primerjana dva pristopa pri združevanju zadetkov iskanja: z enačbo in z algoritmom TDR, nato pa je primerjana še izvirna metoda.

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Section: Modelling Application Of Genetic Programmingmentioning

confidence: 99%

“…Comparison of search and ranking solutions is usually based on the measures P@k, MAP, NDCG@k [20] that is used to determine the value of the content. Here, we test the fitness function corresponding to the MAP value.…”

Section: Modelling Application Of Genetic Programmingmentioning

confidence: 99%

New Re-Ranking Approach in Merging Search Results

2019

IJCAI

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“…In this part, we review our previous results in query translation by building a structured query in the target language from a given query in the source language [5].…”

Section: Building Structured Query In the Target Languagementioning

confidence: 99%

“…We use 50 Vietnamese queries with an average length of 8,73 words for our experiment. At first, we apply query translation methods in [5] to build structured queries in English. After that, we follow the algorithm presented in section 3 to change query term weights.…”

Section: A Test Configurationmentioning

confidence: 99%

“…This work is motivated by our previous results in query translation by building a structured query in the target language from a given query [5]. In this paper, we apply the pseudo-relevance feedback technique to improve the structured query translation by learning weights for query terms from top documents returned by the initial retrieval.…”

Section: Introductionmentioning

confidence: 99%

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Improve Cross Language Information Retrieval With Pseudo-Relevance Feedback

Giang¹,

Hung²,

Pháp³

2016

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin 2015

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Building Proximity Models for Cross Language Information Retrieval

Giang¹,

Hung²,

Pháp³

2015

JST

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In information retrieval systems, the proximity of query terms has been employed to enable ranking models to go beyond the ”bag of words” assumption and it can promote scores of documents where the matched query terms are close to each other. In this article, we study the integration of proximity models into cross-language information retrieval systems. The new proximity models are proposed and incorporated into existing cross-language information systems by combining the proximity score and the original score to re-rank retrieved documents. The experiment results show that the proposed models can help to improve the retrieval performance by 4%-7%, in terms of Mean Average Precision.

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Building Structured Query in Target Language for Vietnamese – English Cross Language Information Retrieval Systems

Cited by 3 publications

References 10 publications

New Re-Ranking Approach in Merging Search Results

New Re-Ranking Approach in Merging Search Results

Improve Cross Language Information Retrieval With Pseudo-Relevance Feedback

Building Proximity Models for Cross Language Information Retrieval

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