Query-dependent learning to rank for cross-lingual information retrieval

Ghanbari, Elham; Shakery, Azadeh

doi:10.1007/s10115-018-1232-8

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…For another part of an IR system, the selective approach to Learning-to-Rank (LTR), which reranks retrieved documents with a learned model, is presented ( Peng, Macdonald & Ounis, 2010 ; Balasubramanian & Allan, 2010 ; Ghanbari & Shakery, 2019 ). As in the term weighting models, different queries take advantage of each ranking function differently and selective methods are studied to decide appropriate function on a per query basis.…”

Section: Related Workmentioning

confidence: 99%

A selective approach to stemming for minimizing the risk of failure in information retrieval systems

Göksel

Arslan

Dinçer

2023

PeerJ Computer Science

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Stemming is supposed to improve the average performance of an information retrieval system, but in practice, past experimental results show that this is not always the case. In this article, we propose a selective approach to stemming that decides whether stemming should be applied or not on a query basis. Our method aims at minimizing the risk of failure caused by stemming in retrieving semantically-related documents. The proposed work mainly contributes to the IR literature by proposing an application of selective stemming and a set of new features that derived from the term frequency distributions of the systems in selection. The method based on the approach leverages both some of the query performance predictors and the derived features and a machine learning technique. It is comprehensively evaluated using three rule-based stemmers and eight query sets corresponding to four document collections from the standard TREC and NTCIR datasets. The document collections, except for one, include Web documents ranging from 25 million to 733 million. The results of the experiments show that the method is capable of making accurate selections that increase the robustness of the system and minimize the risk of failure (i.e., per query performance losses) across queries. The results also show that the method attains a systematically higher average retrieval performance than the single systems for most query sets.

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Section: Related Workmentioning

confidence: 99%

A selective approach to stemming for minimizing the risk of failure in information retrieval systems

Göksel

Arslan

Dinçer

2023

PeerJ Computer Science

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“…However, our method directly uses LTR methods for learning a scoring function that can be used for ranking documents. Recently, Ghanbari and Shakery (2018) used LTR for training a ranking model for CLIR. They define various features for creating a model.…”

Section: Previous Workmentioning

confidence: 99%

A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

2019

Self Cite

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Cross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.

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“…For the image captioning task, Miyazaki et al [17] and Lan et al [18] present that training in a crosslingual learning [19][20][21] manner could lead to a better performance of the model. Cross-lingual learning can be widely applied to natural language processing fields, such as cross-lingual sentiment analysis [19], cross-lingual information retrieval [21], and cross-lingual named entity identification [20]. It could bridge the semantic gap between different languages to some extent, via information complementary and sharing.…”

Section: Introductionmentioning

confidence: 99%

Cross-Lingual Image Caption Generation Based on Visual Attention Model

Wang

Zhang

et al. 2020

IEEE Access

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As an interesting and challenging problem, generating image caption automatically has attracted increasingly attention in natural language processing and computer vision communities. In this paper, we propose an end-to-end deep learning approach for image caption generation. We leverage image feature information at specific location every moment and generate the corresponding caption description through a semantic attention model. The end-to-end framework allows us to introduce an independent recurrent structure as an attention module, derived by calculating the similarity between image feature sequence and semantic word sequence. Additionally, our model is designed to transfer the knowledge representation obtained from the English portion into the Chinese portion to achieve the cross-lingual image captioning. We evaluate the proposed model on the most popular benchmark datasets. We report an improvement of 3.9% over existing state-of-the-art approaches for cross-lingual image captioning on the Flickr8k CN dataset on CIDEr metric. The experimental results demonstrate the effectiveness of our attention model.

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Query-dependent learning to rank for cross-lingual information retrieval

Cited by 8 publications

References 40 publications

A selective approach to stemming for minimizing the risk of failure in information retrieval systems

A selective approach to stemming for minimizing the risk of failure in information retrieval systems

A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

Cross-Lingual Image Caption Generation Based on Visual Attention Model

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