[Published online Journal of Computer Chemistry, Japan Vol.22, 21-23, by J-STAGE]
<Title:> 化学系特許中の表及びテキストからの材料知識データ抽出
<Author(s):> 我妻 正太郎, 竹内 理
<Corresponding author E-Mill:> shotaro.agatsuma.hd(at)hitachi.com
<Abstract:> Material Informatics (MI) needs large amount of data about materials. However, the cost of data extraction is very high. Therefore, chemical researchers are interested in technology to automatically extract the data from published documents such as patents. Previous technologies can extract the data from text in patents, but not tables. Therefore, we develop the data for MI extraction method from texts and tables in patents. In our evaluation, our method can reduce the time of data extraction by one-half. In the results, it can be expected that the new method can sufficiently reduce the cost of data extraction.
<Keywords:> Materials informatics, Machine learning, Natural Language Processing, Data Extraction
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0023/_article/-char/ja/
<Title:> 化学系特許中の表及びテキストからの材料知識データ抽出
<Author(s):> 我妻 正太郎, 竹内 理
<Corresponding author E-Mill:> shotaro.agatsuma.hd(at)hitachi.com
<Abstract:> Material Informatics (MI) needs large amount of data about materials. However, the cost of data extraction is very high. Therefore, chemical researchers are interested in technology to automatically extract the data from published documents such as patents. Previous technologies can extract the data from text in patents, but not tables. Therefore, we develop the data for MI extraction method from texts and tables in patents. In our evaluation, our method can reduce the time of data extraction by one-half. In the results, it can be expected that the new method can sufficiently reduce the cost of data extraction.
<Keywords:> Materials informatics, Machine learning, Natural Language Processing, Data Extraction
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0023/_article/-char/ja/
