The paper defines a set-to-set cosine similarity via projections onto linear spans and applies it to paraphrase detection, but the experiments are too weak to support the claimed advantages.
Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. The proposed method was developed and trained on 1500 text queries of Promobot users in Russian to classify them into 20 categories (classes). As a result, the use of the method allowed to completely solve the problem of misclassification for queries with combining known and unknown features of the model. The theoretical substantiation of the method is presented by the formulated and proved theorem On the Model with Limited Knowledge. It states, that in conditions of limited data, an equal number of equally unknown features of an object cannot have different significance for the classification problem.
fields
cs.LG 1years
2019 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase Task
The paper defines a set-to-set cosine similarity via projections onto linear spans and applies it to paraphrase detection, but the experiments are too weak to support the claimed advantages.