# Information Extraction from Scientific Literature for Method Recommendation

arXiv: https://arxiv.org/abs/1901.00401 · HTML: https://mlsanity.com/paper/1901.00401/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application](https://mlsanity.com/paper/2209.03687/) (2022)
- [DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance](https://mlsanity.com/paper/2202.10163/) (2022)
- [KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction](https://mlsanity.com/paper/2210.02830/) (2022)
- [All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction](https://mlsanity.com/paper/2311.08189/) (2023)
- [Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions](https://mlsanity.com/paper/2207.04029/) (2022)

## Abstract (arXiv)

As a research community grows, more and more papers are published each year. As a result there is increasing demand for improved methods for finding relevant papers, automatically understanding the key ideas and recommending potential methods for a target problem. Despite advances in search engines, it is still hard to identify new technologies according to a researcher's need. Due to the large variety of domains and extremely limited annotated resources, there has been relatively little work on leveraging natural language processing in scientific recommendation. In this proposal, we aim at making scientific recommendations by extracting scientific terms from a large collection of scientific papers and organizing the terms into a knowledge graph. In preliminary work, we trained a scientific term extractor using a small amount of annotated data and obtained state-of-the-art performance by leveraging large amount of unannotated papers through applying multiple semi-supervised approaches. We propose to construct a knowledge graph in a way that can make minimal use of hand annotated data, using only the extracted terms, unsupervised relational signals such as co-occurrence, and structural external resources such as Wikipedia. Latent relations between scientific terms can be learned from the graph. Recommendations will be made through graph inference for both observed and unobserved relational pairs.