Entity–Relation Extraction—A Novel and Lightweight Method Based on a Gate Linear Mechanism

Peng, Guangming; Chen, Xiong

doi:10.3390/electronics9101637

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…PID [28] is one of the most important algorithms which are widely applied in the field of automatic control. The output has a discrete form [29] shown in Eq.…”

Section: Pid Algorithm and Popularitymentioning

confidence: 99%

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

Peng,

Xu,

Chen

et al. 2024

Tsinghua Sci. Technol.

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Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.

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“…PID [28] is one of the most important algorithms which are widely applied in the field of automatic control. The output has a discrete form [29] shown in Eq.…”

Section: Pid Algorithm and Popularitymentioning

confidence: 99%

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

Peng,

Xu,

Chen

et al. 2024

Tsinghua Sci. Technol.

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“…Relational triples' extraction [29] is a task in which factual knowledge is mined from texts. It is a well-studied task in information extraction.…”

Section: Related Workmentioning

confidence: 99%

A Triple Relation Network for Joint Entity and Relation Extraction

Wang

Yang

et al. 2022

Electronics

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Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the extracted entities, which will cause incompleteness in succeeding Knowledge Graphs’ (KGs) construction. Since relevant triples give a clue for establishing implicit connections among entities, we propose a Triple Relation Network (Trn) to jointly extract triples, especially handling extracting implicit triples. Specifically, we design an attention-based entity pair encoding module to identify all normal entity pairs directly. To construct implicit connections among these extracted entities in triples, we utilize our triple reasoning module to calculate relevance between two triples. Then, we select the top-K relevant triple pairs and transform them into implicit entity pairs to predict the corresponding implicit relations. We utilize a bipartite matching objective to match normal triples and implicit triples with the corresponding labels. Extensive experiments demonstrate the effectiveness of the proposed method on two public benchmarks, and our proposed model significantly outperforms previous strong baselines.

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“…Han et al [19] propose a network model based on the dilate gated convolutional neural network, in which the word representations and depth of the network are expanded to improve the performance. Peng et al [20] combine CNN with gate linear mechanism to accelerate the encoding of text.…”

Section: Related Workmentioning

confidence: 99%

TDJEE: A Document-Level Joint Model for Financial Event Extraction

2021

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Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

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Entity–Relation Extraction—A Novel and Lightweight Method Based on a Gate Linear Mechanism

Cited by 5 publications

References 21 publications

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

A Triple Relation Network for Joint Entity and Relation Extraction

TDJEE: A Document-Level Joint Model for Financial Event Extraction

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