Two developments in computational text analysis widen opportunities for qualitative data
analysis: amounts of digital text worth investigating are growing rapidly, and progress in …

We demonstrate a method for collaborative ranking of future events. Previous work on
recommender systems typically relies on feedback on a particular item, such as a movie …

Electronic health records (EHRs) contain important clinical information about patients. Some
of these data are in the form of free text and require preprocessing to be able to used in …

The proliferation of Web applications based on short texts represents both an opportunity
and a challenge to text mining algorithms, because of sparse representations and lack of …

Electronic health records (EHRs) contain important clinical information about patients.
Efficient and effective use of this information could supplement or even replace manual chart …

Semi-supervised text classification uses both labeled and unlabeled data to construct
classifiers. The key issue is how to utilize the unlabeled data. Clustering based classification …

Today many businesses have adopted Twitter as a new marketing channel to promote their
products and services. One of the potentially useful applications is to recommend users to …

We propose an unsupervised feature generation algorithm using the repositories of human
knowledge for effective text categorization. Conventional bag of words (BOW) depends on …

Among the features of documents is their logical structure, which represents their
components such as chapters, sections, paragraphs, titles, chapter titles, etc. The titles and …

Pre-trained language models, which provide dense embedded representations of words in
context, achieve state-of-the-art results on countless NLP tasks. While highly effective, these …