Acquiring Bilingual Named Entity Translations from Content-Aligned Corpora

Kumano, Tadashi; Kashioka, Hideki; Tanaka, Hideki; Fukusima, Takahiro

doi:10.1007/978-3-540-30211-7_19

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Huang et al [11] proposed a method that acquires English-Chinese NE pairs from a parallel corpus, based on a linear combination of costs related to NEs transliteration, translation, and tagging. Kumano et al [13] proposed a method for acquiring bilingual NE translations from non-literal contentaligned parallel corpora. First, they recognized the NEs in each of a bilingual document pair.…”

Section: Related Workmentioning

confidence: 99%

Improved Statistical Machine Translation by Cross-Linguistic Projection of Named Entities Recognition and Translation

Sellami

Deffaf

Sadat

et al. 2015

CyS

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One of the existing difficulties in natural language processing applications is the lack of appropriate tools for the recognition, translation, and/or transliteration of named entities (NEs), specifically for lessresourced languages. In this paper, we propose a new method to automatically label multilingual parallel data for Arabic-French pair of languages with named entity tags and build lexicons of those named entities with their transliteration and/or translation in the target language. For this purpose, we bring in a third well-resourced language, English, that might serve as pivot, in order to build an Arabic-French NE Translation lexicon. Evaluations on the Arabic-French pair of languages using English as pivot in the transitive model showed the effectiveness of the proposed method for mining Arabic-French named entities and their translations. Moreover, the integration of this component in statistical machine translation outperformed the baseline system.

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Section: Related Workmentioning

confidence: 99%

Improved Statistical Machine Translation by Cross-Linguistic Projection of Named Entities Recognition and Translation

Sellami

Deffaf

Sadat

et al. 2015

CyS

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“…Huang, Vogel, and Waibel (2003) described a multi-feature NE alignment model to extract NE equivalences (with translation, transliteration, and tagging features), from which a NE translation dictionary was then constructed. Kumano et al (2004) proposed a method to extract English-Chinese NE pairs from a content-aligned corpus. This approach tries to find the correspondences between bilingual NE groups based on the similarity in their order of appearance in each document.…”

Section: Related Workmentioning

confidence: 99%

A Joint Model to Identify and Align Bilingual Named Entities

Chen

Zong

Su³

2013

Computational Linguistics

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In this article, an integrated model is derived that jointly identifies and aligns bilingual named entities (NEs) between Chinese and English. The model is motivated by the following observations: (1) whether an NE is translated semantically or phonetically depends greatly on its entity type, (2) entities within an aligned pair should share the same type, and (3) the initially detected NEs can act as anchors and provide further information while selecting NE candidates. Based on these observations, this article proposes a translation mode ratio feature (defined as the proportion of NE internal tokens that are semantically translated), enforces an entity type consistency constraint, and utilizes additional new NE likelihoods (based on the initially detected NE anchors).Experiments show that this novel method significantly outperforms the baseline. The typeinsensitive F-score of identified NE pairs increases from 78.4% to 88.0% (12.2% relative improvement) in our Chinese-English NE alignment task, and the type-sensitive F-score increases from 68.4% to 83.0% (21.3% relative improvement). Furthermore, the proposed model demonstrates its robustness when it is tested across different domains. Finally, when semi-supervised learning is conducted to train the adopted English NE recognition model, the proposed model also significantly boosts the English NE recognition type-sensitive F-score.

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“…The ONs are extracted from two corpora. The corpora can be parallel corpora [Moore et al, 2003] or contentaligned corpora [Kumano et al, 2004]. The third type of methods introduces the web resources into ON translation.…”

Section: Related Workmentioning

confidence: 99%

A Chinese-English organization name translation system using heuristic web mining and asymmetric alignment

Yang

Zhao

Liu

2009

Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural

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In this paper, we propose a novel system for translating organization names from Chinese to English with the assistance of web resources. Firstly, we adopt a chunkingbased segmentation method to improve the segmentation of Chinese organization names which is plagued by the OOV problem. Then a heuristic query construction method is employed to construct an efficient query which can be used to search the bilingual Web pages containing translation equivalents. Finally, we align the Chinese organization name with English sentences using the asymmetric alignment method to find the best English fragment as the translation equivalent. The experimental results show that the proposed method outperforms the baseline statistical machine translation system by 30.42%.

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Acquiring Bilingual Named Entity Translations from Content-Aligned Corpora

Cited by 6 publications

References 10 publications

Improved Statistical Machine Translation by Cross-Linguistic Projection of Named Entities Recognition and Translation

Improved Statistical Machine Translation by Cross-Linguistic Projection of Named Entities Recognition and Translation

A Joint Model to Identify and Align Bilingual Named Entities

A Chinese-English organization name translation system using heuristic web mining and asymmetric alignment

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