# TAG: Toward Accurate Social Media Content Tagging with a Concept Graph

arXiv: https://arxiv.org/abs/2110.06892 · HTML: https://mlsanity.com/paper/2110.06892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations](https://mlsanity.com/paper/1511.08277/) (2015)
- [AliCoCo: Alibaba E-commerce Cognitive Concept Net](https://mlsanity.com/paper/2003.13230/) (2020)
- [MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching](https://mlsanity.com/paper/1905.10289/) (2019)
- [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](https://mlsanity.com/paper/1905.08487/) (2019)
- [Bidirectional LSTM-CRF for Clinical Concept Extraction](https://mlsanity.com/paper/1611.08373/) (2016)
- [GIANT: Scalable Creation of a Web-scale Ontology](https://mlsanity.com/paper/2004.02118/) (2020)
- [DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging](https://mlsanity.com/paper/1707.04596/) (2017)
- [Matching Natural Language Sentences with Hierarchical Sentence Factorization](https://mlsanity.com/paper/1803.00179/) (2018)
- [Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development](https://mlsanity.com/paper/1807.10661/) (2018)

## Built on by (most cited first, 1 of 1)

- [SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging](https://mlsanity.com/paper/2609.36982/) (2026)

## Abstract (arXiv)

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.