A Chinese keyphrase extraction method for multimodal information enhancement representation

ZHOU Xuanyu; LIU Lin; LU Xiao; LI Xuan; ZHANG Siming

doi:10.16511/j.cnki.qhdxxb.2024.27.015

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Journal of Tsinghua University(Science and Technology) ›› 2024, Vol. 64 ›› Issue (10) : 1785-1796. DOI: 10.16511/j.cnki.qhdxxb.2024.27.015

SPECIAL SECTION: BIG DATA ANALYTICS

A Chinese keyphrase extraction method for multimodal information enhancement representation

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Abstract

[Objective] At present, China is undergoing a critical digital transformation in education. This shift has led to an explosive growth of educational content online, presenting a challenge for researchers who find it increasingly difficult to sift through massive amounts of text data. The necessity to quickly grasp important information has made keyphrase extraction an invaluable tool. Keyphrase extraction automates the process of identifying words or phrases that encapsulate the main themes of a text, proving critical for text retrieval, text summary, and other tasks. Despite its importance, the current keyphrase extraction tasks mainly rely on pretrained language models to obtain text representation. These models are often trained based on a generic text corpus and struggle to adapt to specific domains according to the characteristics of downstream tasks owing to their limited ability to capture the subtle semantic representation of single-mode information. Therefore, developing methods for accurate and efficient keyphrase extraction from massive texts remains a pressing research challenge. [Methods] This paper presents a novel approach for Chinese keyphrase extraction, dubbed multimodal information enhancement representation for keyphrase extraction (MIEnhance-KPE). Our method first deconstructs characters into radicals using a character splitting dictionary and extracts radical features through a convolutional neural network. At the same time, we integrate a trainable adapter layer between the transformer layers of a pretrained language model. Through the above operations, the bottom level semantic features of the pretrained language model and radical features are fully integrated to obtain a domain adaptive text representation. Characters are then transformed into glyph images representing different periods in history and writing styles. Subsequently, we employ group convolution to extract the glyphic features of these characters. Meanwhile, a cross-attention mechanism is used to fuse the glyphic and text features, yielding richer and more comprehensive semantic representations. The final step involves using a conditional random field model to learn the relationship between the fused features and labels. Through sequence labeling, we identify candidate keyphrases, ranking them based on position and word frequency weight to determine the most relevant keyphrases. [Results] MIEnhance-KPE's performance was tested using two datasets: the published Chinese Scientific Literature (CSL) and the self-constructed Chinese Education Keyphrase Extraction Dataset (CEKED). Our method demonstrated a substantial improvement compared to the most advanced keyphrase extraction methods, with F values increasing by 15.71% and 3.40% on the CSL and CEKED datasets, respectively. Ablation experiments further confirmed the effectiveness of both the domain adaptive module and the visual semantic enhancement module in enhancing keyphrase extraction accuracy. In addition, this paper explored various methods for fusing glyphic and semantic features, concluding that the cross-attention mechanism excels in adaptively merging different features to improve task accuracy. [Conclusions] The MIEnhance-KPE proposed in this paper can considerably improve the accuracy of keyphrase extraction tasks. This aids educational researchers in quickly locating relevant literature and understanding the cutting-edge trends of educational development. Additionally, MIEnhance-KPE introduces a novel approach to literature analysis in the educational sector. It provides a solid data foundation for examining the motivation of educational reform and innovation, thereby accelerating the digital transformation process in education.

Key words

Chinese keyphrase extraction / multimodal information / multigranularity semantic features / cross-attention mechanism / domain adaptation

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ZHOU Xuanyu, LIU Lin, LU Xiao, LI Xuan, ZHANG Siming. A Chinese keyphrase extraction method for multimodal information enhancement representation[J]. Journal of Tsinghua University(Science and Technology). 2024, 64(10): 1785-1796 https://doi.org/10.16511/j.cnki.qhdxxb.2024.27.015

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