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Related Experiment Video

Updated: Sep 6, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Word centrality constrained representation for keyphrase extraction.

Zelalem Gero¹, Joyce C Ho¹

¹Emory University.

Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting

|June 24, 2022

Summary

This summary is machine-generated.

This study introduces a new keyphrase extraction model that improves accuracy for short documents by adding a centrality constraint to Bidirectional long short-term memory networks. The enhanced model outperforms existing methods in literature search and discovery.

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Area of Science:

Natural Language Processing
Information Retrieval
Computational Linguistics

Background:

Automated methods are crucial for managing the growing volume of digital documents.
Keyphrase extraction identifies salient concepts but supervised methods struggle with short texts due to unclear context.

Purpose of the Study:

To develop an improved keyphrase extraction model for enhanced document search and discovery.
To address the limitations of existing methods in handling short documents.

Main Methods:

Proposed a novel keyphrase extraction model incorporating a centrality constraint.
Enriched word representations using Bidirectional long short-term memory (BiLSTM) networks with the centrality constraint.

Main Results:

The proposed model demonstrated superior performance compared to state-of-the-art approaches.
Evaluated on two public datasets, confirming the model's effectiveness.

Conclusions:

The centrality constraint effectively enhances keyphrase extraction, particularly for short documents.
The model offers a significant improvement for literature search, discovery, and mining.