A Structure-Based Model for Chinese Organization Name Translation

Chen, Yufeng; Zong, Chengqing

doi:10.1145/1330291.1330292

Cited by 13 publications

(7 citation statements)

References 32 publications

(28 reference statements)

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“…Templates and common rules are learned from bilingual ONs without any syntactically annotated training data; special rules, which are defined in CFG format based on word level, are applied to cover more organization name translation. This study shows the effectiveness and the improvement of the alignment acquiring performance between Chinese-English NEs [9].…”

Section: Traditional Multilingual Ne Extraction Methodsmentioning

confidence: 65%

“…Chen et al [9] systematically studied the characteristics of Chinese organization names (ONs). They defined appropriate chunk-units for analyzing the internal structure of Chinese organization names, to make the chunk-based organization name translation feasible and flexible via a hierarchical context-free grammar (CFG) derivation.…”

Section: Traditional Multilingual Ne Extraction Methodsmentioning

confidence: 99%

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An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Chen

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYNamed Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora. key words: named entity translation equivalents acquisition, Chinese Hanzi and Japanese Kanji mapping table, inductive learning, monolingual corpora

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Section: Traditional Multilingual Ne Extraction Methodsmentioning

confidence: 65%

Section: Traditional Multilingual Ne Extraction Methodsmentioning

confidence: 99%

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Chen

et al. 2017

IEICE Trans. Inf. & Syst.

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“…Chen and Zong investigated a structural formulation of ONs and presented a hierarchical structure-based ON translation model for Chinese-English translation [13]. First, it divides the ONs into three types of chunks.…”

Section: Relevant Researchmentioning

confidence: 99%

Improving Translation of Organization Names Combining Translation Model and Web Mining

Li¹,

Zhou²,

Ma³

et al. 2016

IJDTA

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“…Their studies focused on transformation rules with particular attention to distinguishing translated parts from transliterated parts; the performance of ruleapplication in NE translation was not described, however. Chen and Zong (2008) proposed a chunk-based probabilistic translation model for organization names, although, its application to person and location names has not been studied.…”

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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A Structure-Based Model for Chinese Organization Name Translation

Cited by 13 publications

References 32 publications

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Improving Translation of Organization Names Combining Translation Model and Web Mining

A Joint Model to Identify and Align Bilingual Named Entities

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