93 research outputs found
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Event detection (ED), a sub-task of event extraction, involves identifying
triggers and categorizing event mentions. Existing methods primarily rely upon
supervised learning and require large-scale labeled event datasets which are
unfortunately not readily available in many real-life applications. In this
paper, we consider and reformulate the ED task with limited labeled data as a
Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical
Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn
better prototypes for event types, but also produce more robust sentence
encodings for event mentions. Differing from vanilla prototypical networks
simply computing event prototypes by averaging, which only consume event
mentions once, our model is more robust and is capable of distilling contextual
information from event mentions for multiple times due to the multi-hop
mechanism of DMNs. The experiments show that DMB-PN not only deals with sample
scarcity better than a series of baseline models but also performs more
robustly when the variety of event types is relatively large and the instance
quantity is extremely small.Comment: Accepted by WSDM 202
GIANT: Scalable Creation of a Web-scale Ontology
Understanding what online users may pay attention to is key to content
recommendation and search services. These services will benefit from a highly
structured and web-scale ontology of entities, concepts, events, topics and
categories. While existing knowledge bases and taxonomies embody a large volume
of entities and categories, we argue that they fail to discover properly
grained concepts, events and topics in the language style of online population.
Neither is a logically structured ontology maintained among these notions. In
this paper, we present GIANT, a mechanism to construct a user-centered,
web-scale, structured ontology, containing a large number of natural language
phrases conforming to user attentions at various granularities, mined from a
vast volume of web documents and search click graphs. Various types of edges
are also constructed to maintain a hierarchy in the ontology. We present our
graph-neural-network-based techniques used in GIANT, and evaluate the proposed
methods as compared to a variety of baselines. GIANT has produced the Attention
Ontology, which has been deployed in various Tencent applications involving
over a billion users. Online A/B testing performed on Tencent QQ Browser shows
that Attention Ontology can significantly improve click-through rates in news
recommendation.Comment: Accepted as full paper by SIGMOD 202
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