My opinion on which is easier to use is biased, but regarding Ivan Akcheurov's answer, we only released Stanford CoreNLP in Oct 2010, so it isn't very old. Regarding his suggestions, it seems to depend on whether you want to be using a higher-level processing framework or actual processing tools. E.g., if you poke around Knime, it appears that the only NLP components included are actually OpenNLP ones, and most of the machine learning is wrapping Weka.... For groups of individual tools that work together, Stanford NLP, OpenNLP, NLTK, and Lingpipe are perhaps the main choices.
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我对哪个更容易使用的看法是有偏见的，但关于 Ivan Akcheurov 的回答，我们在 2010 年 10 月才发布了斯坦福 CoreNLP，所以它并不是很老。关于他的建议，似乎取决于您是否想要使用更高级别的处理框架或实际的处理工具。例如，如果你浏览 Knime，似乎包含的唯一 NLP 组件实际上是 OpenNLP 的，大部分机器学习都在包装 Weka.... 对于协同工作的单独工具组，Stanford NLP、OpenNLP、NLTK 和 Lingpipe 可能是主要选择。
My opinion on which is easier to use is biased, but regarding Ivan Akcheurov's answer, we only released Stanford CoreNLP in Oct 2010, so it isn't very old. Regarding his suggestions, it seems to depend on whether you want to be using a higher-level processing framework or actual processing tools. E.g., if you poke around Knime, it appears that the only NLP components included are actually OpenNLP ones, and most of the machine learning is wrapping Weka.... For groups of individual tools that work together, Stanford NLP, OpenNLP, NLTK, and Lingpipe are perhaps the main choices.
我建议您 GATE (gate.ac.uk):
GATE
OpenNLP
LingPipe
NLTK
I suggest you GATE (gate.ac.uk):
GATE
OpenNLP
LingPipe
NLTK
我建议你斯坦福大学，因为它在一个开源软件包下提供了多种功能，例如斯坦福 CoreNLP 有
StanFord Parser。Stanford POS Tagger。Stanford 命名实体识别< /代码>。斯坦福类型依赖项。等等。简而言之，在一个保护伞下，您可以获得多个解决方案......
I suggest you Stanford as it provides the multiple things under one package that is opensource also e.g. Stanford CoreNLP has
StanFord Parser.Stanford POS Tagger.Stanford Named Entity Recognition.Stanford Typed Dependencies. etc.So in short under one umbrella you get multiple Solutions....