Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (PTLM), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic …
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing …
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 networks (NNs) make it possible to build reliable models without handcrafted …
Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks. Recent literature on this topic has reached a consensus that node contents and link structures should be integrated …
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, …
Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in …
With the recent advances in information networks, the problem of identifying group structure or communities has received a significant amount of attention. Most of the existing principles of community detection or clustering mainly focus on either …