Automatic Translation of Scholarly Terms into Patent Terms

Nanba, Hidetsugu; Kamaya, Hideaki; Takezawa, Toshiyuki; Okumura, Manabu; Shinmori, Akihiro; Tanigawa, Hidekazu

doi:10.1007/978-3-642-19231-9_19

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Wei et al [33] use word alignment between queries to identify synonyms. Similarly, word alignment can be coupled with machine translation to identify synonyms by translating text into a second language and then back into the original language (e.g., [23]). While word alignment methods have been shown to perform well, their applicability is limited due to requiring either query logs or parallel corpora.…”

Section: Related Workmentioning

confidence: 99%

Relevance-Ranked Domain-Specific Synonym Discovery

Yates

Goharian

2014

Lecture Notes in Computer Science

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Interest in domain-specific search is growing rapidly, creating a need for domain-specific synonym discovery. The best-performing methods for this task rely on query logs and are thus difficult to use in many circumstances. We propose a method for domain-specific synonym discovery that requires only a domain-specific corpus. Our method substantially outperforms previously proposed methods in realistic evaluations. Due to the difficulty of identifying pairs of synonyms from among a large number of terms, methods have traditionally been evaluated by their ability to choose a target term's synonym from a small set of candidate terms. We generalize this evaluation by evaluating methods' performance when required to choose a target term's synonym from progressively larger sets of candidate terms. We approach synonym discovery as a ranking problem and evaluate the methods' ability to rank a target term's candidate synonyms. Our results illustrate that while our proposed method substantially outperforms existing methods, synonym discovery is still a difficult task to automate and is best coupled with a human moderator.

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Section: Related Workmentioning

confidence: 99%

Relevance-Ranked Domain-Specific Synonym Discovery

Yates

Goharian

2014

Lecture Notes in Computer Science

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“…As another approach for cross-genre information retrieval, Nanba et al (2009) proposed a method to paraphrase scholarly terms into patent terms (e.g., paraphrasing "floppy disc" into "magnetic recording medium"). They focused on citation relationships between research papers and patents for the paraphrased terms.…”

Section: Cross-genre Information Accessmentioning

confidence: 99%

Classification of research papers into a patent classification system using two translation models

Nanba

Takezawa

2009

Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries - NLPIR4DL '09

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Classifying research papers into patent classification systems enables an exhaustive and effective invalidity search, prior art search, and technical trend analysis. However, it is very costly to classify research papers manually. Therefore, we have studied automatic classification of research papers into a patent classification system. To classify research papers into patent classification systems, the differences in terms used in research papers and patents should be taken into account. This is because the terms used in patents are often more abstract or creative than those used in research papers in order to widen the scope of the claims. It is also necessary to do exhaustive searches and analyses that focus on classification of research papers written in various languages. To solve these problems, we propose some classification methods using two machine translation models. When translating English research papers into Japanese, the performance of a translation model for patents is inferior to that for research papers due to the differences in terms used in research papers and patents. However, the model for patents is thought to be useful for our task because translation results by patent translation models tend to contain more patent terms than those for research papers. To confirm the effectiveness of our methods, we conducted some experiments using the data of the Patent Mining Task in the NTCIR-7 Workshop. From the experimental results, we found that our method using translation models for both research papers and patents was more effective than using a single translation model.

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Limitations of Automatic Patent IR

Jürgens¹,

Womser‐Hacker²

2014

Datenbank Spektrum

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Automatic Translation of Scholarly Terms into Patent Terms

Cited by 3 publications

References 6 publications

Relevance-Ranked Domain-Specific Synonym Discovery

Relevance-Ranked Domain-Specific Synonym Discovery

Classification of research papers into a patent classification system using two translation models

Limitations of Automatic Patent IR

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