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    <p>If you want to try tagging without the context, you are looking for some sort of a unigram tagger, aka <code>looup tagger</code>. <strong>A unigram tagger tags a word solely based on the frequency of the tag given a word</strong>. So it avoids the context heuristics, however for any tagging task you must have data. And for the unigrams you need annotated data to train it. See the <code>lookup tagger</code> in the nltk tutorial <a href="http://nltk.googlecode.com/svn/trunk/doc/book/ch05.html" rel="nofollow noreferrer">http://nltk.googlecode.com/svn/trunk/doc/book/ch05.html</a>. </p> <p>Below is another way of training/testing an unigram tagger in <code>NLTK</code></p> <pre><code>&gt;&gt;&gt; from nltk.corpus import brown &gt;&gt;&gt; from nltk import UnigramTagger as ut &gt;&gt;&gt; brown_sents = brown.tagged_sents() # Split the data into train and test sets. &gt;&gt;&gt; train = int(len(brown_sents)*90/100) # use 90% for training # Trains the tagger &gt;&gt;&gt; uni_tag = ut(brown_sents[:train]) # this will take some time, ~1-2 mins # Tags a random sentence &gt;&gt;&gt; uni_tag.tag ("this is a foo bar sentence .".split()) [('this', 'DT'), ('is', 'BEZ'), ('a', 'AT'), ('foo', None), ('bar', 'NN'), ('sentence', 'NN'), ('.', '.')] # Test the taggers accuracy. &gt;&gt;&gt; uni_tag.evaluate(brown_sents[train+1:]) # evaluate on 10%, will also take ~1-2 mins 0.8851469586629643 </code></pre> <p>I wouldn't recommend using WordNet for pos tagging because just are sooo many words that are still has no entry in wordnet. But you can take a look at using lemma frequencies in wordnet, see <a href="https://stackoverflow.com/questions/15551195/how-to-get-the-wordnet-sense-frequency-of-a-synset-in-nltk">How to get the wordnet sense frequency of a synset in NLTK?</a>. These frequencies are based on the SemCor corpus (<a href="http://www.cse.unt.edu/~rada/downloads.html" rel="nofollow noreferrer">http://www.cse.unt.edu/~rada/downloads.html</a>) </p>
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    1. CO"A unigram tagger tags a word solely based on the frequency of the tag given a word." Ideally it would also look at the word endings, for smoothing.
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    2. COa morphological based unigram tagger, it is =)
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