# Multi-Task Learning of Keyphrase Boundary Classification

arXiv: https://arxiv.org/abs/1704.00514 · HTML: https://mlsanity.com/paper/1704.00514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [End-to-End NLP Knowledge Graph Construction](https://mlsanity.com/paper/2106.01167/) (2021)
- [CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding](https://mlsanity.com/paper/2105.10912/) (2021)
- [Determining the Credibility of Science Communication](https://mlsanity.com/paper/2105.14473/) (2021)
- [Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task](https://mlsanity.com/paper/2201.02305/) (2022)
- [TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)](https://mlsanity.com/paper/2301.06902/) (2022)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)

## Abstract (arXiv)

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.