A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase Task
The following paper presents a method of comparing two sets of vectors. The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors. It may be applicable when we compare the meanings of two sentences as part of the problem of paraphrasing. This is the problem of measuring semantic similarity of two sentences (group of words). The existing methods are not sensible for the word order or syntactic connections in the considered sentences. The method appears to be advantageous because it neither presents a group of words as one scalar value, nor does it try to show the closeness through an aggregation vector, which is mean for the set of vectors. Instead of that we measure the cosine of the angle as the mean for the first group vectors projections (the context) on one side and each vector of the second group on the other side. The similarity of two sentences defined by these means does not lose any semantic characteristics and takes account of the words traits. The method was verified on the comparison of sentence pairs in Russian.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic SimilaritySemantic Textual SimilaritySentenceSimilar Papers 제목 키워드 기반
Heterogeneous Information Network Embedding for Meta Path based Proximity
A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original …
Network EmbeddingMultidimensional scaling of two-mode three-way asymmetric dissimilarities: finding archetypal profiles and clustering
Multidimensional scaling visualizes dissimilarities among objects and reduces data dimensionality. While many methods address symmetric proximity data, asymmetric and especially three-way proximity data (capturing relati…
Computational EfficiencyDenoising and Completion of 3D Data via Multidimensional Dictionary Learning
In this paper a new dictionary learning algorithm for multidimensional data is proposed. Unlike most conventional dictionary learning methods which are derived for dealing with vectors or matrices, our algorithm, named K…
DenoisingDictionary LearningImage DenoisingRelative Navigation and Dynamic Target Tracking for Autonomous Underwater Proximity Operations
Estimating a target's 6-DoF motion in underwater proximity operations is difficult because the chaser lacks target-side proprioception and the available relative observations are sparse, noisy, and often partial (e.g., U…
Proximity Matters: Local Proximity Preserved Balancing for Treatment Effect Estimation
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizing distribution discrepancies between tr…
counterfactualSelection bias