# An Explanatory Query-Based Framework for Exploring Academic Expertise

arXiv: https://arxiv.org/abs/2105.13728 · HTML: https://mlsanity.com/paper/2105.13728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space](https://mlsanity.com/paper/1909.11258/) (2019)
- [A Scalable Hybrid Research Paper Recommender System for Microsoft Academic](https://mlsanity.com/paper/1905.08880/) (2019)
- [Unsupervised, Efficient and Semantic Expertise Retrieval](https://mlsanity.com/paper/1608.06651/) (2016)
- [WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking](https://mlsanity.com/paper/1805.03947/) (2018)
- [paper2repo: GitHub Repository Recommendation for Academic Papers](https://mlsanity.com/paper/2004.06059/) (2020)
- [Effective Distributed Representations for Academic Expert Search](https://mlsanity.com/paper/2010.08269/) (2020)
- [New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding](https://mlsanity.com/paper/2004.03621/) (2020)
- [Learning to Rank Academic Experts in the DBLP Dataset](https://mlsanity.com/paper/1501.05132/) (2015)

## Abstract (arXiv)

The success of research institutions heavily relies upon identifying the right researchers "for the job": researchers may need to identify appropriate collaborators, often from across disciplines; students may need to identify suitable supervisors for projects of their interest; administrators may need to match funding opportunities with relevant researchers, and so on. Usually, finding potential collaborators in institutions is a time-consuming manual search task prone to bias. In this paper, we propose a novel query-based framework for searching, scoring, and exploring research expertise automatically, based upon processing abstracts of academic publications. Given user queries in natural language, our framework finds researchers with relevant expertise, making use of domain-specific knowledge bases and word embeddings. It also generates explanations for its recommendations. We evaluate our framework with an institutional repository of papers from a leading university, using, as baselines, artificial neural networks and transformer-based models for a multilabel classification task to identify authors of publication abstracts. We also assess the cross-domain effectiveness of our framework with a (separate) research funding repository for the same institution. We show that our simple method is effective in identifying matches, while satisfying desirable properties and being efficient.