Cross-Lingual Information Retrieval by Feature Vectors

Lilleng, Jeanine; Tomassen, Stein L.

doi:10.1007/978-3-540-73351-5_20

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…We use Bing Translator because has a good efficiency when translating English to Spanish or vice versa, and also because provides a wide range of language pairs for translation. Also, we use Google Translator because their high efficiency has been tested in other NLP tasks [27,28].…”

Section: Cross-lingual Textual Entailmentmentioning

confidence: 99%

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Castillo

2011

Int. J. Mach. Learn. & Cyber.

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In this paper we explain how to build a recognizing textual entailment (RTE) system which only uses semantic similarity measures based on WordNet. We show how the widely used WordNet-based semantic measures can be generalized to build sentence level semantic metrics in order to be used in both mono-lingual and cross-lingual textual entailment. We experiment with a wide variety of RTE datasets and evaluate the contribution of an algorithm which expands the RTE monolingual corpus. Results achieved with this method yielded significant statistical differences when predicting RTE test sets. We provide an efficiency analysis of these metrics drawing some conclusions about their practical utility in recognizing textual entailment. We also analyze the cross-lingual textual entailment task, we create a bilingual English-Spanish corpus, and propose a procedure to create a cross-lingual textual entailment corpus for any pair of languages. Finally, we show that the proposed method is enough to build an average score RTE system in both monolingual and cross-lingual textual entailment, that uses semantic information from WordNet as the only source of lexicalsemantic knowledge.

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Section: Cross-lingual Textual Entailmentmentioning

confidence: 99%

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Castillo

2011

Int. J. Mach. Learn. & Cyber.

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“…In these methods, we face with the problem of translation ambiguity in which a single word in one language has more than one translation in the other language (Pourmahmoud and Shamsfard, 2008). By using information external to the query, like ontologies and document collections, the effect of ambiguity can be reduced (Lilleng and Tomassen, 2007). Ontology is a formal, explicit specification of a shared conceptualization.…”

Section: Jcsmentioning

confidence: 99%

“…Cheng et al (2008) adopt bilingual domain ontology and Muller and Gurevych (2008) used Wikipedia and Wiktionary as a specific domain. Closed or restricted domains CLIR approaches do traditionally produce a better result compared to CLIR used in open domains (Lilleng and Tomassen, 2007) Despite these promising results, they are highly depended on a fairly common terminology being used. Lilleng and Tomassen (2007) and Tomassen et al (2006) investigates query translation in CLIR, caused by ambiguity and polysemy, based on feature vectors and their method uses context during the translation of queries.…”

Section: Related Workmentioning

confidence: 99%

“…Closed or restricted domains CLIR approaches do traditionally produce a better result compared to CLIR used in open domains (Lilleng and Tomassen, 2007) Despite these promising results, they are highly depended on a fairly common terminology being used. Lilleng and Tomassen (2007) and Tomassen et al (2006) investigates query translation in CLIR, caused by ambiguity and polysemy, based on feature vectors and their method uses context during the translation of queries. Tomassen et al (2006) Use a query enrichment approach to associate every concept of the ontology with a feature vector to tailor these concepts to the specific terminology used in the document collection.…”

Section: Related Workmentioning

confidence: 99%

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Query Translation Using Concepts Similarity Based on Quran Ontology for Cross-Language Information Retrieval

Yahya

Abdullah

Azman

et al. 2013

Journal of Computer Science

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In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact on the accuracy of follow-up retrieval results. In dictionary-based approach, we are dealing with the words that have more than one meaning which can decrease the retrieval performance if the query translation return an incorrect translations. These issues need to be overcome using efficient technique. In this study we proposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in English and Malay languages as a bilingual parallel-corpora and Quran concepts as a resource for cross-language query translation along with dictionary-based translation. For evaluation, we measure the performance of three IR systems. IR₁ is natural language query IR, IR₂ is natural language query CLIR based on dictionary (as a Baseline) and IR₃ is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our proposed method brings significant improvement in retrieval accuracy for English document collections, but deficient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language

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“…Our focus to CLTE is based on free online (web) machine translation systems, so we chose Microsoft Bing Translator 2 , because it has a good efficiency when translating English to Spanish or vice-versa, and also because provides a wide range of language pairs for translation. In addition, we use Google Translate 3 , because his high efficiency has been tested in other NLP tasks [5] and [6].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

Castillo¹,

Cardenas²

2011

Polibits

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In this paper, we show an approach to cross-lingual textual entailment (CLTE) by using machine translation systems such as Bing Translator and Google Translate. We experiment with a wide variety of data sets to the task of textual Entailment (TE) and evaluate the contribution of an algorithm that expands a monolingual TE corpus that seems promising for the task of CLTE. We built a CLTE corpus and we report a procedure that can be used to create a CLTE corpus in any pair of languages. We also report the results obtained in our experiments with the three-way classification task for CLTE and we show that this result outperform the average score of RTE (Recognizing Textual Entailment) systems. Finally, we find that using WordNet as the only source of lexical-semantic knowledge it is possibly to build a system for CLTE, which achieves comparable results with the average score of RTE systems for both two-way and three-way tasks.

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Cross-Lingual Information Retrieval by Feature Vectors

Cited by 7 publications

References 16 publications

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Query Translation Using Concepts Similarity Based on Quran Ontology for Cross-Language Information Retrieval

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

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