The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Determining semantic similarity between texts is important in many tasks in information
retrieval such as search, query suggestion, automatic summarization and image finding …

Twitter is among the fastest‐growing microblogging and online social networking services.
Messages posted on Twitter (tweets) have been reporting everything from daily life stories to …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

In microblogging services such as Twitter, the users may become overwhelmed by the raw
data. One solution to this problem is the classification of short text messages. As short texts …

This paper presents a general framework for building classifiers that deal with short and
sparse text & Web segments by making the most of hidden topics discovered from large …

In recent years, online social networking has revolutionized interpersonal communication.
The newer research on language analysis in social media has been increasingly focusing …

Computing the degree of semantic relatedness of words is a key functionality of many
language applications such as search, clustering, and disambiguation. Previous …

Content in microblogging systems such as Twitter is produced by tens to hundreds of
millions of users. This diversity is a notable strength, but also presents the challenge of …