Although conceptualization has been widely studied in semantics and knowledge
representation, it is still challenging to find the most accurate concept
phrases to characterize the main idea of a text snippet on the fast-growing
social media. This is partly attributed to the fact that most knowledge bases
contain general terms of the world, such as trees and cars, which do not have
the defining power or are not interesting enough to social media app users.
Another reason is that the intricacy of natural language allows the use of
tense, negation and grammar to change the logic or emphasis of language, thus
conveying completely different meanings. In this paper, we present TAG, a
high-quality concept matching dataset consisting of 10,000 labeled pairs of
fine-grained concepts and web-styled natural language sentences, mined from the
open-domain social media. The concepts we consider represent the trending
interests of online users. Associated with TAG is a concept graph of these
fine-grained concepts and entities to provide the structural context
information. We evaluate a wide range of popular neural text matching models as
well as pre-trained language models on TAG, and point out their insufficiency
to tag social media content with the most appropriate concept. We further
propose a novel graph-graph matching method that demonstrates superior
abstraction and generalization performance by better utilizing both the
structural context in the concept graph and logic interactions between semantic
units in the sentence via syntactic dependency parsing. We open-source both the
TAG dataset and the proposed methods to facilitate further research.

本文针对社交媒体上的文本片段难以准确表达主题这一问题，提出了一种基于 TAG 概念匹配数据集和概念图的图图匹配方法，该方法在利用概念图结构和句法依赖解析中的语义单元逻辑交互方面表现出更好的抽象和概括性能。