Feature-Less End-to-End Nested Term Extraction

TL;DR

This paper introduces a deep learning end-to-end approach for automatic term extraction that supports nested terms without requiring additional features, achieving high recall and comparable precision on raw text.

Contribution

The proposed method is novel in supporting nested term extraction in an end-to-end manner without relying on extra features.

Findings

Achieves high recall in term extraction

Maintains comparable precision with existing methods

Supports nested term extraction effectively

Abstract

In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested term extraction and does not crucially need extra (extracted) features. Results show that it can achieve high recall and a comparable precision on term extraction task with inputting segmented raw text.