Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Recent years have seen an exponential growth (+ 98% in 2022 wrt the previous year) of the
number of research articles in the few-shot learning field, which aims at training machine …

Linguistic sequence labeling is a general approach encompassing a variety of problems,
such as part-of-speech tagging and named entity recognition. Recent advances in neural …

Data augmentation techniques have been widely used to improve machine learning
performance as they enhance the generalization capability of models. In this work, to …

Aspect sentiment triplet extraction is an important task in natural language processing.
Previous work tends to focus on the interaction between the aspect and opinion, while …

Recent advances in deep neural models allow us to build reliable named entity recognition
(NER) systems without handcrafting features. However, such methods require large …

Portfolio management (PM) is a fundamental financial planning task that aims to achieve
investment goals such as maximal profits or minimal risks. Its decision process involves …

Weakly supervised text classification based on a few user-provided seed words has recently
attracted much attention from researchers. Existing methods mainly generate pseudo-labels …

In this paper, we study automatic keyphrase generation. Although conventional approaches
to this task show promising results, they neglect correlation among keyphrases, resulting in …

With the rise of the hydrogen energy industry, countries worldwide have introduced relevant
policies to elevate the development of the hydrogen energy industry to the height of national …