Text data has been growing drastically in the present day because of digitalization. The
Internet, being flooded with millions of documents every day, makes the task of text …

This paper outlines a conceptual framework for understanding recent developments in
information retrieval and natural language processing that attempts to integrate dense and …

With the rise of technology, anyone can easily share their sentiments through social media
platforms like Facebook, Twitter, LinkedIn, Google+, and Instagram. Sentiment analysis is a …

Reducing user effort in finding relevant information is one of the key objectives of search
systems. Existing approaches have been shown to effectively exploit the context from the …

A human is capable of understanding and classifying a text but a computer can understand
the underlying semantics of a text when texts are represented in a way comprehensible by …

The representation of words as vectors, conventionally known as word embeddings, has
drawn considerable attention in recent years as feature learning techniques for natural …

To mitigate the problem of over-dependence of a pseudo-relevance feedback algorithm on
the top-M document set, we make use of a set of equivalence classes of queries rather than …

Findings from randomized controlled trials (RCTs) of behaviour change interventions
encode much of our knowledge on intervention efficacy under defined conditions. Predicting …

An information gap exists across Wikipedia's language editions, with a considerable
proportion of articles available in only a few languages. As an illustration, it has been …

A standard word embedding algorithm, such as 'word2vec', embeds each word as a dense
vector of a preset dimensionality, the components of which are learned by maximizing the …