隨著資訊開放，每年都有大量可供索引的文章被發表。為此，能夠汲取重要資料供資料庫存取的資訊擷取技術顯得愈來愈重要。在既有的資訊擷取工作模式中，研究單位由程式處理大量的自然語言文獻，再聘請領域專家學者進行驗證，這樣工作模式難以長時間維持。
為解決這個問題，本研究套用了一個應用群眾外包概念的資訊擷取模型，試圖以群眾力量處理資訊擷取關鍵的第一步──命名實體辨識。基於這個模型，本研究設計並實作出一款手機遊戲──馬克菌，以遊戲樂趣作為群眾外包獎勵，讓玩家在遊戲的過程中，願意自主協助生醫命名實體辨識學術工作。參考了市面上數個成功遊戲的實例，馬克菌將繁重的標記工作，與一個有趣的寵物養成遊戲結合。
在驗證馬克菌效果的封閉測試實驗中，鎖定蛋白質相關文獻的蛋白質名稱辨識。實驗結果顯示馬克菌可以正常運作與遊玩，玩家犯下的錯誤可以透過被系統機制彼此修正，令足量的玩家可以達到與專家相當的水平，且當玩家數量愈多，精確度也將有所提升。這些回收的結構性資料得以在後續的應用中，建構出文字探勘資料庫，回饋領域專家學者使用。

Biomedical articles have dramatically increased recently, driving many tools for automatically extracting valuable knowledge in natural language articles. However, to make such full automatically extracted data be considered as knowledge, a manual verification step is generally required. This manual verification, which needs to employ many domain experts, may cost more than developing exaction algorithms and is hard to last for a long time. This work aims to introduce crowdsourcing, which borrows energy from community, to solve this problem. A mobile game, Markteria was implemented to extract knowledge from natural language documents “crowdsourcingly”. In this work, named-entity recognition (NER) was chosen to be the task, since it’s a required prior step for information extraction applications. Markteria is topic independent and can be extended to other NER topic in the future.

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