A new hybrid semi-supervised algorithm for text classification with class-based semantics

Vector Space Models (VSM) are commonly used in language processing to represent certain aspects of natural language semantics. Semantics of VSM comes from the distributional hypothesis, which states that words that occur in similar contexts usually have similar meanings. In our previous work, we pro...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Knowledge-based systems Ročník 108; s. 50 - 64
Hlavní autoři: Altınel, Berna, Ganiz, Murat Can
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.09.2016
Témata:
Class-based transformations
Semantic smoothing kernel
Semantics
Semi-supervised classification
Text classification
Semi-supervised classification
Class-based transformations
Semantics
Semantic smoothing kernel
Text classification
ISSN:0950-7051, 1872-7409
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Vector Space Models (VSM) are commonly used in language processing to represent certain aspects of natural language semantics. Semantics of VSM comes from the distributional hypothesis, which states that words that occur in similar contexts usually have similar meanings. In our previous work, we proposed novel semantic smoothing kernels based on classspecific transformations. These kernels use class-term matrices, which can be considered as a new type of VSM. By using the class as the context, these methods can extract class specific semantics by making use of word distributions both in documents and in different classes. In this study, we adapt two of these semantic classification approaches to build a novel and high performance semi-supervised text classification algorithm. These approaches include Helmholtz principle based calculation of term meanings in the context of classes for initial classification and a supervised term weighting based semantic kernel with Support Vector Machines (SVM) for the final classification model. The approach used in the first phase is especially good at learning with very small datasets, while the approach in the second phase is specifically good at eliminating noise in a relatively large and noisy training sets when building a classification model. Overall, as a semantic semi-supervised learning algorithm, our approach can effectively utilize abundant source of unlabeled instances to improve the classification accuracy significantly especially when the amount of labeled instances are limited.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2016.06.021