# Entity Set Co-Expansion in StackOverflow

arXiv: https://arxiv.org/abs/2212.02271 · HTML: https://mlsanity.com/paper/2212.02271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion](https://mlsanity.com/paper/1910.08194/) (2019)
- [SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble](https://mlsanity.com/paper/1910.08192/) (2019)
- [Empower Entity Set Expansion via Language Model Probing](https://mlsanity.com/paper/2004.13897/) (2020)
- [HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories](https://mlsanity.com/paper/1910.07115/) (2019)
- [Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion](https://mlsanity.com/paper/2001.10106/) (2020)
- [Term Set Expansion based NLP Architect by Intel AI Lab](https://mlsanity.com/paper/1808.08953/) (2018)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [Unsupervised Key Event Detection from Massive Text Corpora](https://mlsanity.com/paper/2206.04153/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [Ontology Enrichment for Effective Fine-grained Entity Typing](https://mlsanity.com/paper/2310.07795/) (2023)

## Abstract (arXiv)

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.