A Distribution-Based Threshold for Determining Sentence Similarity
- URL: http://arxiv.org/abs/2311.16675v1
- Date: Tue, 28 Nov 2023 10:42:35 GMT
- Title: A Distribution-Based Threshold for Determining Sentence Similarity
- Authors: Gioele Cadamuro and Marco Gruppo
- Abstract summary: We present a solution to a semantic textual similarity (STS) problem in which it is necessary to match two sentences containing, as the only distinguishing factor, highly specific information.
The solution revolves around the use of a neural network, based on the siamese architecture, to create the distributions of the distances between similar and dissimilar pairs of sentences.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We hereby present a solution to a semantic textual similarity (STS) problem
in which it is necessary to match two sentences containing, as the only
distinguishing factor, highly specific information (such as names, addresses,
identification codes), and from which we need to derive a definition for when
they are similar and when they are not. The solution revolves around the use of
a neural network, based on the siamese architecture, to create the
distributions of the distances between similar and dissimilar pairs of
sentences. The goal of these distributions is to find a discriminating factor,
that we call "threshold", which represents a well-defined quantity that can be
used to distinguish vector distances of similar pairs from vector distances of
dissimilar pairs in new predictions and later analyses. In addition, we
developed a way to score the predictions by combining attributes from both the
distributions' features and the way the distance function works. Finally, we
generalize the results showing that they can be transferred to a wider range of
domains by applying the system discussed to a well-known and widely used
benchmark dataset for STS problems.
Related papers
- Measuring similarity between embedding spaces using induced neighborhood graphs [10.056989400384772]
We propose a metric to evaluate the similarity between paired item representations.
Our results show that accuracy in both analogy and zero-shot classification tasks correlates with the embedding similarity.
arXiv Detail & Related papers (2024-11-13T15:22:33Z) - Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos [63.94040814459116]
Self-supervised methods have shown remarkable progress in learning high-level semantics and low-level temporal correspondence.
We propose a novel semantic-aware masked slot attention on top of the fused semantic features and correspondence maps.
We adopt semantic- and instance-level temporal consistency as self-supervision to encourage temporally coherent object-centric representations.
arXiv Detail & Related papers (2023-08-19T09:12:13Z) - Random Ferns for Semantic Segmentation of PolSAR Images [0.0]
This paper extends the Random Fern framework to the semantic segmentation of polarimetric synthetic aperture radar images.
Two distinct optimization strategies are proposed.
Experiments show that results can be achieved that are similar to a more complex Random Forest model.
arXiv Detail & Related papers (2022-02-07T20:22:57Z) - Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic
Uncertainty [58.144520501201995]
Bi-Lipschitz regularization of neural network layers preserve relative distances between data instances in the feature spaces of each layer.
With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices.
We also propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution.
arXiv Detail & Related papers (2021-10-12T22:04:19Z) - Kernel distance measures for time series, random fields and other
structured data [71.61147615789537]
kdiff is a novel kernel-based measure for estimating distances between instances of structured data.
It accounts for both self and cross similarities across the instances and is defined using a lower quantile of the distance distribution.
Some theoretical results are provided for separability conditions using kdiff as a distance measure for clustering and classification problems.
arXiv Detail & Related papers (2021-09-29T22:54:17Z) - The Exploitation of Distance Distributions for Clustering [3.42658286826597]
In cluster analysis, different properties for distance distributions are judged to be relevant for appropriate distance selection.
By systematically investigating this specification using distribution analysis through a mirrored-density plot, it is shown that multimodal distance distributions are preferable in cluster analysis.
Experiments are performed on several artificial datasets and natural datasets for the task of clustering.
arXiv Detail & Related papers (2021-08-22T06:22:08Z) - Unsupervised Anomaly Detection From Semantic Similarity Scores [0.0]
We present a simple and generic framework, it SemSAD, that makes use of a semantic similarity score to carry out anomaly detection.
We are able to outperform previous approaches for anomaly, novelty, or out-of-distribution detection in the visual domain by a large margin.
arXiv Detail & Related papers (2020-12-01T13:12:31Z) - Toward Scalable and Unified Example-based Explanation and Outlier
Detection [128.23117182137418]
We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction.
We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
arXiv Detail & Related papers (2020-11-11T05:58:17Z) - Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation [67.83872616307008]
Unversarial Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain.
In this paper, we propose a novel Adrial Dual Distincts Network (AD$2$CN) to align the source and target domain data distribution simultaneously with matching task-specific category boundaries.
To be specific, a domain-invariant feature generator is exploited to embed the source and target data into a latent common space with the guidance of discriminative cross-domain alignment.
arXiv Detail & Related papers (2020-08-27T01:29:10Z) - Spatially Adaptive Inference with Stochastic Feature Sampling and
Interpolation [72.40827239394565]
We propose to compute features only at sparsely sampled locations.
We then densely reconstruct the feature map with an efficient procedure.
The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
arXiv Detail & Related papers (2020-03-19T15:36:31Z)
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.