The Web as a Parallel Corpus

Resnik, Philip; Smith, Noah A.

doi:10.1162/089120103322711578

Cited by 366 publications

(250 citation statements)

References 21 publications

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“…In fact, several researchers have shown that using the Web as a corpus is an effective way of addressing the typical data sparseness problem one encounters when working with corpora (compare [14], [21], [23], [25]). Actually, we subscribe to the principal idea by Markert et al [23] of exploiting the Google TM API.…”

Section: Finding Patternsmentioning

confidence: 99%

Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

242

170

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The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata. In this paper we propose PANKOW (Pattern-based Annotation through Knowledge on the Web), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.

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Section: Finding Patternsmentioning

confidence: 99%

Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

242

170

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“…For instance developing a technique for mining the web to collect parallel corpora for low-density language pairs (Resnik and Smith, 2003), and running new SMT system for languages Catalan-English with no parallel corpus (Gispert and Mariño, 2006). In a research conducted by Utiyama and Isahara (2007), the use of pivot language through phrase translation and sentence translation are investigated.…”

Section: Previous Workmentioning

confidence: 99%

Persian–Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language

Ahmadnia

Serrano

Haffari

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian-English SMT is combined with the English-Spanish one, where the relatively large corpora of each may be used in support of the Persian-Spanish pairing. Our results indicate that the pivot language technique outperforms the direct SMT processes currently in use between Persian and Spanish. Furthermore, we investigate the sentence translation pivot strategy and the phrase translation in turn, and demonstrate that, in the context of the Persian-Spanish SMT system, the phrase-level pivoting outperforms the sentence-level pivoting. Finally we suggest a method called combination model in which the standard direct model and the best triangulation pivoting model are blended in order to reach a high-quality translation.

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“…Resnik and Smith proposed a method to extract similar documents from Web based on the HTML [7]. Talvensaari et al proposed a method to find similar documents based on translated words in the source language to the target language by using the main keywords [4].…”

Section: Related Workmentioning

confidence: 99%

Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources

Cheon

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.

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The Web as a Parallel Corpus

Cited by 366 publications

References 21 publications

Towards the self-annotating web

Towards the self-annotating web

Persian–Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language

Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources

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