  More than 80% of today's data is unstructured in nature, and these
unstructured datasets evolve over time. A large part of these datasets are text
documents generated by media outlets, scholarly articles in digital libraries,
findings from scientific and professional communities, and social media. Vector
space models were developed to analyze text data using data mining and machine
learning algorithms. While ample vector space models exist for text data, the
evolutionary aspect of ever-changing text corpora is still missing in
vector-based representations. The advent of word embeddings has enabled us to
create a contextual vector space, but the embeddings fail to consider the
temporal aspects of the feature space successfully. This paper presents an
approach to include temporal aspects in feature spaces. The inclusion of the
time aspect in the feature space provides vectors for every natural language
element, such as words or entities, at every timestamp. Such temporal word
vectors allow us to track how the meaning of a word changes over time, by
studying the changes in its neighborhood. Moreover, a time-reflective text
representation will pave the way to a new set of text analytic abilities
involving time series for text collections. In this paper, we present a
time-reflective vector space model for temporal text data that is able to
capture short and long-term changes in the meaning of words. We compare our
approach with the limited literature on dynamic embeddings. We present
qualitative and quantitative evaluations using the tracking of semantic
evolution as the target application.

Computation and Language;Information Retrieval