Combining n-grams and deep convolutional features for language variety classification
نویسندگان
چکیده
منابع مشابه
Using Character n-grams and Style Features for Gender and Language Variety Classification
Author profiling is the problem of determining the characteristics of an author of an anonymous text. In this paper, we detail a method to determine the language variety and the gender of the authors of tweets, as a submission for the Author Profiling Task at PAN 2017. This method seeks to select the most significant character n-grams for each class considered, combining them with style feature...
متن کاملSyntactic Dependency-Based N-grams as Classification Features
In this paper we introduce a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner of what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking the words as they appear in the text. Dependency trees fit directly into this idea, while in case of constituency...
متن کاملProtein classification using modified n-grams and skip-grams.
Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce...
متن کاملAggregating Deep Convolutional Features for Image Retrieval
Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation appr...
متن کاملDeep Convolutional Decision Jungle for Image Classification
We propose a novel method called deep convolutional decision jungle (CDJ) and its learning algorithm for image classification. The CDJ maintains the structure of standard convolutional neural networks (CNNs), i.e. multiple layers of multiple responsemaps fully connected. Each responsemap—or node—in both the convolutional and fully-connected layers selectively respond to class labels s.t. each d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Natural Language Engineering
سال: 2019
ISSN: 1351-3249,1469-8110
DOI: 10.1017/s1351324919000299