Cross-Lingual Information Retrieval: A Dictionary-Based Query Translation Approach

Sharma, Vijay K; Mittal, Namita

doi:10.1007/978-981-10-3773-3_59

Cited by 17 publications

(4 citation statements)

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“…MRD's can also combine the texts of general and domain specific dictionaries. Generally, a bilingual dictionary consist of list of words expressed in source language and their equivalent translations in target language [157]. These dictionaries may have translation probabilities that enable weighting and disambiguation.…”

Section: ) Dictionary Based Approachmentioning

confidence: 99%

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

et al. 2019

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Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages. INDEX TERMS Ontology, text retrieval, multimedia retrieval, cross lingual retrieval.

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Section: ) Dictionary Based Approachmentioning

confidence: 99%

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

et al. 2019

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“…Pirkola et al [3] adopted recognition of transformation rules and translation equivalents based on frequency to enhance the performance of the dictionary-based approach. Sharma and Mittal [4] built a query translation system based on dictionaries in which the queries were tokenized, and N-gram techniques were adopted to create terms for multiword queries. In the dictionary-based approach, even an optimal dictionary cannot solve the OOV problems related to short, real, diverse, and dynamic queries.…”

Section: Related Workmentioning

confidence: 99%

Study on Unknown Term Translation Mining from Google Snippets

Yao

2019

Information

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Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.

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“…A manual dictionary is used for translation and a transliteration mining algorithm is used to handle Out Of Vocabulary (OOV) words which are not present in the dictionary [16]. The transliteration generation or mining techniques are used to handle the OOV words [3,13,17].…”

Section: Literature Surveymentioning

confidence: 99%

An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

Sharma¹,

Mittal²

2018

CyS

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Cross-Lingual Information Retrieval (CLIR) enables a user to query to the different language target documents. CLIR incorporates a Machine Translation (MT) technique which is in growing state for Indian languages due to the unavailability of enough resources. In this paper, a Statistical Machine Translation (SMT) system is trained on two parallel corpora separately. A large English language corpus is used for language modeling in SMT. Experiments are evaluated by using BLEU score, further, these experimental setups are used to translate the Hindi language queries for the experimental analysis of Hindi-English CLIR. Since SMT does not deal with morphological variants while the proposed Translation Induction Algorithm (TIA) deals with that, therefore, TIA outperforms the SMT systems in perspective of CLIR.

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Cross-Lingual Information Retrieval: A Dictionary-Based Query Translation Approach

Cited by 17 publications

References 5 publications

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Study on Unknown Term Translation Mining from Google Snippets

An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

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