Accurate semantic representation models are essential in text mining applications. For a
successful application of the text mining process, the text representation adopted must keep …

Document classification presents difficult challenges due to the sparsity and the high
dimensionality of text data, and to the complex semantics of the natural language. The …

Term-based representations of documents have found wide-spread use in information
retrieval. However, one of the main shortcomings of such methods is that they largely …

The word-to-vector (W2V) technique represents words as low-dimensional continuous
vectors in such a way that semantic related words are close to each other. This produces a …

In this paper we show how a vector-based word representation obtained via word2vec can
help to improve the results of a document classifier based on bags of words. Both models …

Two document representation methods are mainly used in solving text mining problems.
Known for its intuitive and simple interpretability, the bag-of-words method represents a …

Document representation using the bag-of-words approach may require bringing the
dimensionality of the representation down in order to be able to make effective use of …

To represent a text as a bag of properly identified “phrases” and use the representation for
processing the text is proved to be useful. The key question here is how to identify the …

One of the main themes supporting text mining is text representation, ie, looking for the
appropriate terms to transfer the documents into numerical vectors. Recently, many efforts …

Distributed word representation plays a pivotal role in various natural language processing
tasks. In spite of its success, most existing methods only consider contextual information …