Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules

Tsukimoto, Hiroshi

doi:10.1007/11362197_2

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…We use precision (P), recall (R), and F1 score (F1) to evaluate our model on the TA-CRED dataset and F1 score on the SemEval-2010 Task 8 dataset. For both datasets, we compare our model (CEGCN) against several competitive baselines, which contain logical regression models [24], sequence-based feature extraction models [8,25], the LSTM-based models [10,26], and graph-based models [27,28]. These baselines include the relation extraction model with a dependency tree as the input and the latest improved GCN models.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Four types of models are compared. (1) The logical regression (LR) models [24]: a traditional relation extraction model based on dependency trees combined with lexical information. (2) The CNN-based models [30]: these models use multi-window filters to capture the semantic features of sentences for relational extraction automatically.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…LR [24] 73.5 49.9 59.4 CNN [30] 75.6 47.5 58.3 SDP-LSTM [11] 66.3 52.7 58.7 PA-LSTM [17] 66.0 59.2 62.4 C-GCN [12] 65.7 63.3 66.4 AGGCN [22] 69.9 60.9 65.1 DAGCN [27] 70 In addition, we conducted validation experiments on the SemEval-2010 Task 8 dataset to assess the versatility of our model. As indicated in Table 3, we conducted validation experiments on some relevant dependency models.…”

Section: Model P R F1mentioning

confidence: 99%

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Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

et al. 2022

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Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model that enables entity mentions to obtain semantic-guided contextual information for more accurate relational representations. This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. Then, we employ a dependency tree with entities as global nodes and add virtual edges to construct an entity-centric logical adjacency matrix (ELAM). This matrix can enable entities to aggregate the semantic-guided contextual information with a one-layer GCN calculation. The experimental results on the TACRED and SemEval-2010 Task 8 datasets show that our model can efficiently use semantic-guided contextual information to enrich semantic entity representations and outperform previous models.

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Section: Performance Comparisonmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Model P R F1mentioning

confidence: 99%

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Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

et al. 2022

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“…The model fusion calculates hbrid results based on predicting ratings, C, α and user similarity. According to these parameters, it determines hybrid results by logistic regression [21]. Recommendation is used to recommend items for users.…”

Section: Modulesmentioning

confidence: 99%

A Fast and Better Hybrid Recommender System Based on Spark

Wang

Zhuang

et al. 2016

Lecture Notes in Computer Science

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With the rapid development of information technology, recommender systems have become critical components to solve information overload. As an important branch, weighted hybrid recommender systems are widely used in electronic commerce sites, social networks and video websites such as Amazon, Facebook and Netflix. In practice, developers typically set a weight for each recommendation algorithm by repeating experiments until obtaining better accuracy. Despite the method could improve accuracy, it overly depends on experience of developers and the improvements are poor. What worse, workload will be heavy if the number of algorithms rises. To further improve performance of recommender systems, we design an optimal hybrid recommender system on Spark. Experimental results show that the system can improve accuracy, reduce execution time and handle large-scale datasets. Accordingly, the hybrid recommender system balances accuracy and execution time.

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“…Therefore, how to select the size of the time period becomes an issue worth exploring. Tsukimoto [11] developed a data mining technique called logical regression analysis (LRA), which consists of regression analysis and the approximation method. The LRA used nonparametric regression analysis in functional magnetic resonance imaging (FMRI) to discover the rules of brain functions.…”

Section: Introductionmentioning

confidence: 99%

Regression analysis of the number of association rules

Liu

2011

Int. J. Autom. Comput.

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The typical model, which involves the measures: support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coefficients to test the fitting effects of the equations and uses significance test to verify whether the coefficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coefficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

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Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules

Cited by 4 publications

References 20 publications

Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

A Fast and Better Hybrid Recommender System Based on Spark

Regression analysis of the number of association rules

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