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copied!<p>First you will have to tokenize your text. This may seem trivial (split at any whitespace may work for you) but formally it is harder. Then you have to decide what is a noun. Does "the car park" contain one noun (car park), two nouns (car, park) or one noun (park) and one adjective (car)? This is a hard problem, but again you may be able to get by without it.</p> <p>Does "I saw the xyzzy" identify a noun not in a dictionary? The word "the" probably identifies xyzzy as a noun.</p> <p>Where are the nouns in "time flies like an arrow". Compare with "fruit flies like a banana" (thanks to Groucho Marx).</p> <p>We use the Brown tagger (Java) (<a href="http://en.wikipedia.org/wiki/Brown_Corpus" rel="noreferrer">http://en.wikipedia.org/wiki/Brown_Corpus</a>) in the OpenNLP toolkit (opennlp.tools.lang.english.PosTagger; opennlp.tools.postag.POSDictionary on <a href="http://opennlp.sourceforge.net/" rel="noreferrer">http://opennlp.sourceforge.net/</a>) to find nouns in normal English and I'd recommend starting with that - it does most of your thinking for you. Otherwise look at any of the POSTaggers (<a href="http://en.wikipedia.org/wiki/POS_tagger" rel="noreferrer">http://en.wikipedia.org/wiki/POS_tagger</a>) or (<a href="http://www-nlp.stanford.edu/links/statnlp.html#Taggers" rel="noreferrer">http://www-nlp.stanford.edu/links/statnlp.html#Taggers</a>).</p> <blockquote> <p>In part-of-speech tagging by computer, it is typical to distinguish from 50 to 150 separate parts of speech for English, for example, NN for singular common nouns, NNS for plural common nouns, NP for singular proper nouns (see the POS tags used in the Brown Corpus)</p> </blockquote> <p>There is a very full list of NLP toolkits in <a href="http://en.wikipedia.org/wiki/Natural_language_processing_toolkits" rel="noreferrer">http://en.wikipedia.org/wiki/Natural_language_processing_toolkits</a>. I would strongly suggest you use one of those rather than trying to match against Wordnet or other collections.</p>

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