Entity Translation Mining from Comparable Corpora: Combining Graph Mapping with Corpus Latent Features

Kim, Jinhan; Hwang, Seung-won; Long, Jinlin; Song, Young-In; Zhou, Ming

doi:10.1109/tkde.2012.117

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…For instance, Shao and Ng [23] integrated the E and EC. Lee et al [24] brought together E, EC and You et al [25] and Kim et al [26] not only merged all the features together, but also incorporated the additional latent features into their studies. Table Since Chinese characters contain significant semantic information, Chinese Hanzi and Japanese Kanji mapping tables can be very useful for improving the performance of MT and CLIR.…”

Section: Type Entity Relationshipmentioning

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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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: Type Entity Relationshipmentioning

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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“…Other interesting approaches such as [17], [18] rely on temporal distributions of entities. That is, two entities are considered to be similar if the two entities in different languages have similar occurrence distributions over time.…”

Section: Entity Translationmentioning

confidence: 99%

Overcoming Asymmetry in Entity Graphs

Lee

Rok

Hwang

2014

IEEE Trans. Knowl. Data Eng.

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This paper studies the problem of mining named entity translations by aligning comparable corpora. Current state-of-the-art approaches mine a translation pair by aligning an entity graph in one language to another based on node similarity or propagated similarity of related entities. However, they, building on the assumption of "symmetry", quickly deteriorate on "weakly" comparable corpora with some asymmetry. In this paper, we pursue two directions for overcoming relation and entity asymmetry respectively. The first approach starts from weakly comparable corpora (for high recall) then ensures precision by selective propagation only to entities of symmetric relations. The second approach starts from parallel corpora (for high precision) then enhances recall by extending the translation matrix based on node similarity and contextual similarity. Our experimental results on English-Chinese corpora show that both approaches are effective and complementary. Our combined approach outperforms the best-performing baseline in terms of F1-score by up to 0.28.

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“…• Phonetic Similarity (PH) (Kim et al, 2013) • Off-the-shelf Translator: Google Translate 5 , Bing Translator 6…”

Section: Experimental Settingmentioning

confidence: 99%

Map Translation Using Geo-tagged Social Media

Lee¹,

Lee²,

Hwang³

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Volume 2: Short Pa

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This paper discusses the problem of map translation, of servicing spatial entities in multiple languages. Existing work on entity translation harvests translation evidence from text resources, not considering spatial locality in translation. In contrast, we mine geo-tagged sources for multilingual tags to improve recall, and consider spatial properties of tags for translation to improve precision. Our approach empirically improves accuracy from 0.562 to 0.746 using Taiwanese spatial entities.

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Entity Translation Mining from Comparable Corpora: Combining Graph Mapping with Corpus Latent Features

Cited by 8 publications

References 18 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

Overcoming Asymmetry in Entity Graphs

Map Translation Using Geo-tagged Social Media

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