Computational analysis of linguistic data requires that texts are transformed into numeric
representations. The aim of this research is to evaluate different methods for building vector …

Recently, there has been a lot of activity in learning distributed representations of words in
vector spaces. Although there are models capable of learning high-quality distributed …

This special issue brings together five studies that are the fruit of intense interactions
between two research projects: The 'Feast and Famine'project funded by the UK's Arts and …

Usual text document representations such as tf-idf do not work well in classification tasks for
short-text documents and across diverse data domains. Optimizing different representations …

We present our experience in applying distributional semantics (neural word embeddings)
to the problem of representing and clustering documents in a bilingual comparable corpus …

This paper assesses the performance of frequency and concept based text representation in
Mixed Script Information Retrieval and Classification tasks. In text analytics, representation …

Within a paper we investigate the influence of dimensionality reduction of feature vector
(PCA and random projection) on the results of subject classification of text documents in …

Text data mining is the process of extracting and analyzing valuable information from text. A
text data mining process generally consists of lexical and syntax analysis of input text data …

Lexicon-based methods and word embeddings are the two widely used approaches for
analyzing texts in social media. The choice of an approach can have a significant impact on …

In many natural language processing (NLP) tasks, a document is commonly modeled as a
bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One …