Never-ending language learning
Never-ending language learning
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DOI：
10.1109/bigdata.2014.7004203
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Tom Michael Mitchell;E. Fredkin
中科院分区：
文献类型：
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作者：
Tom Michael Mitchell;E. Fredkin
我们永远不会真正理解学习，直到我们能够制造出能够多年学习许多不同东西的机器，并随着时间的推移成为更好的学习者。我们描述了我们的研究，以建立一个永不结束的语言学习者（NELL），每天运行24小时，永远，学习阅读网页。每一天，NELL都从网络上提取（阅读）更多的事实，进入其不断增长的信念知识库。每一天，内尔也学会了比前一天更好地阅读。NELL已经运行了四年多，每天24小时。到目前为止，结果是7000万个相互关联的信念的集合（例如，servedWtih（coffee，applePie）），NELL在不同的置信水平上进行考虑，沿着数百万个学习过的短语、形态特征和网页结构，NELL使用这些短语、形态特征和网页结构从网络中提取信念。NELL还学习对其提取的知识进行推理，并自动扩展其本体。在http://rtw.ml.cmu.edu跟踪NELL的进展，或在Twitter上关注@CMUNELL。
We will never really understand learning until we can build machines that learn many different things, over years, and become better learners over time. We describe our research to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more facts from the web, into its growing knowledge base of beliefs. Each day NELL also learns to read better than the day before. NELL has been running 24 hours/day for over four years now. The result so far is a collection of 70 million interconnected beliefs (e.g., servedWtih(coffee, applePie)), NELL is considering at different levels of confidence, along with millions of learned phrasings, morphological features, and web page structures that NELL uses to extract beliefs from the web. NELL is also learning to reason over its extracted knowledge, and to automatically extend its ontology. Track NELL's progress at http://rtw.ml.cmu.edu, or follow it on Twitter at @CMUNELL.