課題組高天宇同學等人工作 [32] 則從另一個角度出發，對於開放域的特定新型關係，只需要提供少量精確的實例作為種子，就可以利用預訓練的關係孿生網絡進行滾雪球（Neural SnowBall），從大量無標註文本中歸納出該新型關係的更多實例，不斷迭代訓練出適用於新型關係的關係抽取模型。
總結來說，開放域關係抽取在前深度學習時代取得了一些成效，但如何在深度學習時代與神經網絡模型優勢相結合，有力拓展神經網絡關係抽取模型的泛化能力，值得更多深入探索。
總結
為了更及時地擴展知識圖譜，自動從海量數據中獲取新的世界知識已成為必由之路。以實體關係抽取為代表的知識獲取技術已經取得了一些成果，特別是近年來深度學習模型極大地推動了關係抽取的發展。但是，與實際場景的關係抽取複雜挑戰的需求相比，現有技術仍有較大的局限性。我們亟需從實際場景需求出發，解決訓練數據獲取、少次學習能力、複雜文本語境、開放關係建模等挑戰問題，建立有效而魯棒的關係抽取系統，這也是實體關係抽取任務需要繼續努力的方向。
我們課題組從 2016 年開始耕耘實體關係抽取任務，先後有林衍凱、韓旭、姚遠、曾文遠、張正彥、朱昊、於鵬飛、於志竟成、高天宇、王曉智、吳睿東等同學在多方面開展了研究工作。去年在韓旭和高天宇等同學的努力下，發布了 OpenNRE 工具包 [33]，經過近兩年來的不斷改進，涵蓋有監督關係抽取、遠程監督關係抽取、少次學習關係抽取和文檔級關係抽取等豐富場景。此外，也花費大量科研經費標註了 FewRel（1.0 和 2.0）和 DocRED 等數據集，旨在推動相關方向的研究。
本文總結了我們對實體關係抽取現狀、挑戰和未來發展方向的認識，以及我們在這些方面做出的努力，希望能夠引起大家的興趣，對大家有些幫助。期待更多學者和同學加入到這個領域研究中來。當然，本文沒有提及一個重要挑戰，即以事件抽取為代表的複雜結構的知識獲取，未來有機會我們再專文探討。
限於個人水平，難免有偏頗舛誤之處，還請大家在評論中不吝指出，我們努力改進。需要說明的是，我們沒想把這篇文章寫成嚴謹的學術論文，所以沒有面面俱到把每個方向的所有工作都介紹清楚，如有重要遺漏，還請批評指正。
廣告時間
我們課題組在實體關係抽取方面開展的多項工作（如 FewRel、DocRED 等）是與騰訊微信模式識別中心團隊合作完成的。微信模式識別中心是微信 AI（WeChat AI）下轄的中心之一，主要關注自然語言處理相關的研究和產品。研究方面，他們的研究工作涵蓋對話系統、知識學習、認知推理、機器翻譯等多個方向，今年在 ACL、EMNLP、AAAI 等會議上發表論文 20 多篇，也在多個比賽中獲得優異成績，學術成果頗豐。產品方面，他們開發的小微對話系統和微信對話開放平臺在音箱、公眾號自動客服等場景方面也有不俗的表現，但投入的人力比亞馬遜 Alex 團隊要少得多，也算是對微信「小」團隊做大事風格的一種體現。微信模式識別中心團隊學術與產品雙強的特點也為我們的合作帶來了不一樣的體驗，一方面雙方都對世界前沿技術的研究保持了高度的熱情，能夠一起勠力同心做一些需要時間打磨、但影響深遠的探索，另一方面真實的產品也為我們的研究提供了不同的視角和應用的場景，真正做到了強強聯合、優勢互補，是非常值得合作的團隊。
我們與騰訊微信的這些合作是基於「清華-騰訊聯合實驗室」開展的。我們與騰訊高校合作中心合作多年，參與了包括清華-騰訊聯合實驗室（與清華各院系開展合作的學校級平臺）、犀牛鳥專項基金（面向各類老師的前沿探索研究性項目）、犀牛鳥精英人才培養計劃（面向學生，騰訊和清華雙導師聯合在騰訊培養科研型人才）等項目。感謝騰訊高校合作中心為高校與騰訊搭建的協作共贏的產學研合作平臺，大家可以關注騰訊高校合作微信公眾號 Tencent_UR 了解最新信息。
這裡還要特別介紹我們與微信模識中心的對接人 Patrick Li（由於不可言說的原因只好用他英文名，並非為了裝 B）和林衍凱。Patrick 是我的師弟，跟我已經有十幾年的交情，清華貴系 2005 級本科生，2009 級博士生。他從本科時就加入我們課題組，當時我與他合作發表了我的第一篇 EMNLP 2009 論文，後來他來我們課題組讀博，跟隨孫茂松老師和劉洋老師從事機器翻譯研究，做出很多有影響力的成果。他是我們合作項目的負責人，目前在微信模式識別中心負責領導 NLP 基礎技術的研究和應用工作，在技術和人品方面都可謂有口皆碑，與他合作過的同學都印象深刻。現在，他主要關注自動問答、信息抽取、機器翻譯等方面工作。也許正是受到 Patrick 的」感召「，我們組的林衍凱同學（http://nlp.csai.tsinghua.edu.cn/~lyk/）今年博士畢業後，也選擇加入了他們團隊，也在和我們一起合作開展知識圖譜和自動問答方面的研究工作。
圖窮匕見，讀者中如果有希望找自然語言處理和知識圖譜方面實習或工作的同學，歡迎聯繫 Patrick Li（patrickpli@tencent.com），讓我們共同努力，開展有意思有意義的研究工作。
Patrick Li：http://www.lpeng.net
作者簡介
韓旭，清華大學計算機科學與技術系博士三年級同學，主要研究方向為自然語言處理、知識圖譜、信息抽取。在人工智慧領域國際著名會議 AAAI、ACL、EMNLP、COLING、NAACL 上發表多篇論文，是 OpenKE、OpenNRE 等開源項目的開發者之一。主頁：
https://thucsthanxu13.github.io/thucsthanxu13.github.io
高天宇，清華大學計算機系大四本科生，主要研究方向為自然語言處理、知識圖譜、關係抽取。在人工智慧領域國際著名會議 AAAI、EMNLP 上發表多篇論文，是 OpenNRE 等開源項目的主要開發者之一。主頁：
gaotianyu.xyz
劉知遠，清華大學計算機系副教授、博士生導師。主要研究方向為表示學習、知識圖譜和社會計算。主頁：
nlp.csai.tsinghua.edu.cn
原文連結：https://zhuanlan.zhihu.com/p/91762831
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機器之心「SOTA模型」：22大領域、127個任務，機器學習 SOTA 研究一網打盡。