Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion

• URL: http://arxiv.org/abs/2202.02476v1
• Date: Sat, 5 Feb 2022 03:12:37 GMT
• Title: Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion
• Authors: Peiying Zhang, Xingzhe Huang, Yaqi Wang, Chunxiao Jiang, Shuqing He, Haifeng Wang
• Abstract summary: This paper proposes a novel model based on multi model nonlinear fusion to grasp the meaning of a text from a global perspective. The model uses the Jaccard coefficient based on part of speech, Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm to measure the similarity of sentences. Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.
• Score: 30.71123144365683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language processing (NLP) task has achieved excellent performance in many fields, including semantic understanding, automatic summarization, image recognition and so on. However, most of the neural network models for NLP extract the text in a fine-grained way, which is not conducive to grasp the meaning of the text from a global perspective. To alleviate the problem, the combination of the traditional statistical method and deep learning model as well as a novel model based on multi model nonlinear fusion are proposed in this paper. The model uses the Jaccard coefficient based on part of speech, Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm to measure the similarity of sentences respectively. According to the calculation accuracy of each model, the normalized weight coefficient is obtained and the calculation results are compared. The weighted vector is input into the fully connected neural network to give the final classification results. As a result, the statistical sentence similarity evaluation algorithm reduces the granularity of feature extraction, so it can grasp the sentence features globally. Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.

Related papers

• Modelled Multivariate Overlap: A method for measuring vowel merger [0.0]
  This paper introduces a novel method for quantifying vowel overlap. We evaluate this method on corpus speech data targeting the PIN-PEN merger in four dialects of English.
  arXiv  Detail & Related papers  (2024-06-24T04:56:26Z)
• Multi-label Text Classification using GloVe and Neural Network Models [0.27195102129094995]
  Existing solutions include traditional machine learning and deep neural networks for predictions. This paper proposes a method utilizing the bag-of-words model approach based on the GloVe model and the CNN-BiLSTM network. The method achieves an accuracy rate of 87.26% on the test set and an F1 score of 0.8737, showcasing promising results.
  arXiv  Detail & Related papers  (2023-10-25T01:30:26Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Large-Margin Representation Learning for Texture Classification [67.94823375350433]
  This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The experimental results on texture and histopathologic image datasets have shown that the proposed approach achieves competitive accuracy with lower computational cost and faster convergence when compared to equivalent CNNs.
  arXiv  Detail & Related papers  (2022-06-17T04:07:45Z)
• Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study [3.7881729884531805]
  The paper is organized in a findings-based manner, with each section providing general conclusions. Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models. RF did not perform well in general, confirming the findings in the literature.
  arXiv  Detail & Related papers  (2022-04-27T12:04:33Z)
• Self-interpretable Convolutional Neural Networks for Text Classification [5.55878488884108]
  This paper develops an approach for interpreting convolutional neural networks for text classification problems by exploiting the local-linear models inherent in ReLU-DNNs. We show that our proposed technique produce parsimonious models that are self-interpretable and have comparable performance with respect to a more complex CNN model.
  arXiv  Detail & Related papers  (2021-05-18T15:19:59Z)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)
• Slice Sampling for General Completely Random Measures [74.24975039689893]
  We present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables. The efficacy of the proposed algorithm is evaluated on several popular nonparametric models.
  arXiv  Detail & Related papers  (2020-06-24T17:53:53Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.