Constructing language models from online forms to aid better document representation for more effective clustering

Clustering is the practice of finding tacit patterns in datasets by grouping the corpus by similarity. When clustering documents this is achieved by converting the corpus into a numeric format and applying clustering techniques to this new format. Values are assigned to terms based on their frequency within a particular document, against their general occurrence in the corpus. One obstacle in achieving this aim is as a result of the polysemic nature of terms. That is words having multiple meanings; each intended meaning only being discernible when examining the context in which they are used. Thus, disambiguating the intended meaning of a term can greatly improve the efficacy of a clustering algorithm. One approach to achieve this end has been done through the creation of an ontology - Wordnet, which can act as a look-up as to the intended meaning of a term. Wordnet however, is a static source and does not keep pace with the changing nature of language. The aim of this paper is to show that while Wordnet can be affective, however it is static in nature and thus does not capture some contemporary usage of terms. Particularly when the dataset is taken from online conversation forums, who would not be structured in a standard document format. Our proposed solution involves using Reddit as a contemporary source which moves with new trends in word usage. To better illustrate this point we cluster comments found in online threads such as Reddit and compare the efficacy of different representations of these document sets.