A Method to Construct Chinese-Japanese Named Entity Translation Equivalents Using Monolingual Corpora

Ru, Kuang; Xu, Jinan; Zhang, Yujie; Wu, Peihao

doi:10.1007/978-3-642-41644-6_16

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…In our previous works, we have presented some results of our research [4]. This paper presents some improvements of our study.…”

Section: Introductionmentioning

confidence: 90%

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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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“…In our previous works, we have presented some results of our research [4]. This paper presents some improvements of our study.…”

Section: Introductionmentioning

confidence: 90%

“…We use Precision (P), Recall (R) and F-measure (F), which are defined in Formula (3)(4)(5) as the evaluation criteria of the results. When the parameter β = 1, that is, the precision and recall rate have the same weight, This is known as the F 1 score.…”

Section: Data and Evaluation Criteriamentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents

Xia

Yan

et al. 2015

Natural Language Processing and Chinese Computing

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A Method to Construct Chinese-Japanese Named Entity Translation Equivalents Using Monolingual Corpora

Cited by 2 publications

References 14 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

Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents

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