Double-pass clustering technique for multilingual document collections

Kishida, Kazuaki

doi:10.1177/0165551511404867

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Its fundamental purpose is to determine the similarity between two documents written in different languages. Kishida applied double-pass algorithm to cluster multi-lingual documents – English, French, German and Italian news articles – for document translation [29]. Either the text-translation-based approach or the index-set-mapping approach could be used to perform this task.…”

Section: Literature Reviewmentioning

confidence: 99%

Cross-language patent matching via an international patent classification-based concept bridge

Chen

Chiu

2013

Journal of Information Science

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Patent documents with sophisticated technical information are valuable for developing new technologies and products. They can be written in almost any language, leading to language barrier problems during retrieval. Traditionally, cross-language information retrieval and cross-language document matching have used text-translation-based or index-set-mapping methods. There are several challenges to the traditional methods, however, such as difficulties with natural language translation, complications owing to bilingual or multi-lingual translations (translating between two or more than two languages), and the unavailability of a parallel dual-language document set. This study offers a new and robust solution to cross-language patent document matching: the International Patent Classification (IPC) based concept bridge approach. The proposed method applies Latent Semantic Indexing to extract concepts from each set of patent documents and utilizes the IPC codes to construct a cross-language mediator that expresses patent documents in different languages. Experiments were carried out to demonstrate the performance of the proposed method. There were 3000 English patents and 3000 Chinese patents gathered as training documents from the United States Patent and Trademark Office and the Taiwan Intellectual Property Office, respectively. Another 30 English patents and another 30 Chinese patents were collected to be query patents. Finally, evaluations using an objective measure and subjective judgement were conducted to prove the feasibility and effectiveness of our method. The results show that our method out-performs the traditional text-translation methods.

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Section: Literature Reviewmentioning

confidence: 99%

Cross-language patent matching via an international patent classification-based concept bridge

Chen

Chiu

2013

Journal of Information Science

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“…43 In the context of CLDC, popular approaches consist in exploring word co-occurrence statistics within parallel/comparable corpora. 18,23,35,45 Recent works improved clustering performance by aligning terms from di®erent languages at topic-level. 4,27,29,41 Nonetheless, cross-lingual topic alignment still remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

Document Representation with Statistical Word Senses in Cross-Lingual Document Clustering

Tang

Xia

Wang

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Cross-lingual document clustering is the task of automatically organizing a large collection of multi-lingual documents into a few clusters, depending on their content or topic. It is well known that language barrier and translation ambiguity are two challenging issues for cross-lingual document representation. To this end, we propose to represent cross-lingual documents through statistical word senses, which are automatically discovered from a parallel corpus through a novel cross-lingual word sense induction model and a sense clustering method. In particular, the former consists in a sense-based vector space model and the latter leverages on a sense-based latent † Corresponding author.Dirichlet allocation. Evaluation on the benchmarking datasets shows that the proposed models outperform two state-of-the-art methods for cross-lingual document clustering.

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