擴展OpenNLP組織模型的培訓

Question

我是NLP和OpenNLP庫的新手，目前我正在玩一些功能，特別是圖書館提取組織名稱的功能。如果我用一個簡單的字符串，如

"Bill worked at Microsoft Corp., JP Morgan Chase, Monsanto and General Motors and was amazed at what went on in Congress. " 

我的代碼滴出：

Detected name "Bill". Type person with probability of 0.9604452678787172 
Detected name "Microsoft Corp .". Type organization with probability of 0.9976452599132802 
Detected name "JP Morgan Chase". Type organization with probability of 0.9064399433766583 
Detected name "Monsanto". Type organization with probability of 0.7429123227376515 
Detected name "General Motors". Type organization with probability of 0.965472905375375 
Detected name "Congress". Type organization with probability of 0.9940809804351413 

的一切似乎罰款。但是，如果我切換到世界這樣的更多的英國視圖

"Mark worked at The University of London, HSBC, The Royal Bank of Scotland, Dyson and GlaxoSmithKline." 

我得到

Detected name "Mark". Type person with probability of 0.7496973664676362 
Detected name "London". Type location with probability of 0.6625435519843291 
Detected name "Scotland". Type location with probability of 0.9564118675997605 
Detected name "University of London". Type organization with probability of 0.8516268558212053 
Detected name "Royal Bank". Type organization with probability of 0.8953174632171774 

顯然不是那麼成功。這是因爲組織查找者不瞭解英國機構或者我不幸運？如果前者有辦法讓我採用現有模式，並將其知識擴展到英國機構更好？我快速查看了現有組織模型的培訓數據，但找不到任何內容。

Answer 1

我在快速搜索中也找不到有關培訓數據的任何文檔，但它可能是根據美國報紙文本（華爾街日報或路透社可能來自MUC或CoNLL數據集）進行培訓的，這可以解釋爲什麼它不會對英國實體也是如此。

無法擴展現有模型，但是如果您有註釋數據，則可以使用英國實體來訓練自己的模型。