Tags, a term that most of us bloggers have become familiar with and probably have had a chance to use as well. It mostly and in ideal cases depicts what words or phrases you would associate the given text with. Now for a human mind we have a large choice between terms and a wide variety of words and phrases that we can use to represent one single meaning or tag. This very fact provides us with the challenges of synonym tags, relativity or relationship in tags and retrieving similar meaning tags and phrases that are not necessarily phrased in similar words.

Let us analyze the problem at hand, tags are assigned by the user and are based on the contents of blog according to the perception of the individuals assigning them. According to the distributional hypothesis of linguistics (Harris, 1954) we can say that words expressing similar meanings tend to occur near each-other in similar contexts. If we are to believe the users for their tags then we can assume that the different blogs with similar or same meaning tags will contain similar words. Now if they contain similar words, then question it poses to us is, can we infer using our knowledge of Natural Language Processing that these blogs will exibit similar tags or will be classified under same tag set or tag cloud.

Why are we doing this? There is a lot of text tagged these days and also there is a lot text and other material available on the web that is not tagged. So the aim of the method is to be able to classify that text under some tags may be at a lower priority level or something that will enable the discovery of that text as well based on the tags search. I prefer tag based search as it is more directed than key word based one.

This challenge relates to the classical problem of Natural Language Processing, the “Text classification problem” and we will try and survey if some of the proposed solutions for this and similar problems like Word Sense Disambiguation and similarity measure problems.

For our discussion here let us concentrate on the word vector based methods, WSD or similarity measure. For further discussion here I have a few methods in mind, which are as follows:-

1. Lesks method – one of the very old papers in WSD.

2. Using Co-occurrence vectors to find similarity of word vectors. – Niwa & Nitta 1996, and Wilk’s algorithm.

3. H. Sch¨utze, Automatic word sense discrimination. Computational Linguistics. 1998.

4. Using Wordnet based context vectors to estimate semantic relatedness of concepts. (Patwardhan, Pedersen 2006).