Natural language processing in ROS

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+1 for @fergs answer.

in addition to nltk, i would also look at stanford nlp http://www-nlp.stanford.edu/

to have perfect nlp (i.e. a dialog between user and robot) is really hard. but some simple keyword spotting algorithms + some simple grammars could do most of the interaction with the robot. looking at the use cases you described above (asking for time, askint for place, person, etc). that could be done with a few grammars and keyword spottings (i.e. find the words "what", "who" in the context a simple grammar such as ( what | who | where) ( is | are ) <subject>.

on speech recognition, you can use pocketsphinx, but the accuracy is really low, unless you have to train your own pocketsphinx, which could be very expensive. i would recommend to use sphinx4 (since it comes with a large language model and dictionary already).

otherwise, you can hack siri or android speech recognition and call those services via http for your robot.

at&t and nuance also provide great speech recognition services. they cost a little once you go to production, but for development & research, the usage volume is usually low, and it's almost free. the good thing is that they're very good, so you have a COTS node that you don't have to worry about and focus on your core problem.

KnowRob does not focus on natural-language processing nor dialog with humans, but rather provides a semantic platform for integrating knowledge sources and reasoners. We have investigated the parsing and understanding of natural-language sentences in instructions from the Web a few years ago, the code is available in the comp_ehow package in https://github.com/knowrob/knowrob_ad... . Note, however, that this work aimed at a deeper semantic understanding and the alignment of the resulting descriptions with the knowledge base. Therefore, it may be too slow for fluent natural interaction.