Topic models have been the prominent tools for automatic topic discovery from text corpora.
Despite their effectiveness, topic models suffer from several limitations including the inability …

Due to the exponential growth of scientific publications on the Web, there is a pressing need
to tag each paper with fine-grained topics so that researchers can track their interested fields …

Scientific document classification is a critical task for a wide range of applications, but the
cost of collecting human-labeled data can be prohibitive. We study scientific document …

Large-scale multi-label text classification (LMTC) aims to associate a document with its
relevant labels from a large candidate set. Most existing LMTC approaches rely on massive …

Instead of mining coherent topics from a given text corpus in a completely unsupervised
manner, seed-guided topic discovery methods leverage user-provided seed words to extract …

Instead of relying on human-annotated training samples to build a classifier, weakly
supervised scientific paper classification aims to classify papers only using category …

Weakly-supervised text classification trains a classifier using the label name of each target
class as the only supervision, which largely reduces human annotation efforts. Most existing …

Continual text classification is an important research direction in Web mining. Existing works
are limited to supervised approaches relying on abundant labeled data, but in the open and …

Discovering latent topics from text corpora has been studied for decades. Many existing
topic models adopt a fully unsupervised setting, and their discovered topics may not cater to …

Graphs and texts are two key modalities in data mining. In many cases, the data presents a
mixture of the two modalities and the information is often complementary: in e-commerce …