feature representations
Bag-of-Words and Region-Based Feature Representations in Object Categorization: A Comparative Study
... literature, feature representation can be broadly classified into block/region-based and bag-of-words (BoW) ...different feature representations over different datasets and different image scales ...
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Feature Representations and Logical Compatibility between Temporal Adverbs and Aspects
... In sections 3-1 to 3-4, we classified the temporal adverbs listed by Lu and Ma into four main classes. The subclasses in each main class are somewhat different from Lu and Ma’s although the majority are similar. Lu and ...
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Dependency Parsing of Code Switching Data with Cross Lingual Feature Representations
... The cross-lingual representation learning method is focused on learning cross- lingual features by aligning (or mapping) feature representations (e.g. embedding) between the source and target languages. In ...
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Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
... right feature function for the underly- ing statistical ...good feature combinations (Chen and Man- ning, 2014; Pei et ...its feature function, while the work of Pei et ...sophisticated ...
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A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
... Our system extends the Graph-based parser (Taskar et al., 2005) especially in BIST-parser that works by default with monolingual data. Basi- cally the Graph-based BIST-parser uses bidirec- tional Long Short Term Memory ...
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Comparing the Performance of Feature Representations for the Categorization of the Easy to Read Variety vs Standard Language
... ture representations – bag-of-words, word embeddings, principal components and autoencoders – for the binary categoriza- tion of the easy-to-read variety vs standard ...these feature representations ...
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Learning Condensed Feature Representations from Large Unsupervised Data Sets for Supervised Learning
... ‘condensed feature representations (COFER)’, which is a new and very unique property that can- not be matched by previous semi-supervised learn- ing methods including the iCWR ...noteworthy feature ...
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Learning Transferable Feature Representations Using Neural Networks
... Performance of proposed architecture is compared with standard neural network architecture with one hidden layer (“NN”) (as described in Eq. 1) and a support vector machine (“SVM”) (Chih- Wei Hsu and Lin, 2003) with ...
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Attention Based GRU Network for Domain Adaptation in Sentiment Classification
... Transfer learning differs from semi-supervised learning in that, in terms of solving the scarcity of labeled data, it relaxes the assumption that the training data and test data must obey IDD [1]. Domain adaptation is a ...
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Devanagari Character Recognition in the Wild
... of feature representations are chosen for evaluation – Pixel density [3], Directional features [2], GIST [11], HOG [4], Dense SIFT [9] and Shape Context ...these feature representations to ...
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Leveraging Social Network Data to Alleviate Cold-Start Problem in Recommender Systems
... another feature representation which can be easier for a product recommendation ...product’s feature representations called user embedding and product embedding from the data collected from ...
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Optimizing Dependency Parsing Throughput
... designing feature representations which are optimized for combining single features to more complex feature templates and by optimizing parser ...
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AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
... Unsupervised methods for word embeddings (also called “distributed word representations”) have become popular in natural language processing (NLP). These methods only need very large cor- pora as input to create ...
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SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks
... with feature extraction and classification, the recently developed deep learning architecture [21-23] can automatically learn representations or features from raw data, while jointly performing ...
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Learning Global Features for Coreference Resolution
... In this work, we posit that global context is indeed necessary for further improvements in coreference resolution, but argue that informative cluster, rather than mention, level features are very difficult to de- vise, ...
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Inducing Information Structures for Data driven Text Analysis
... structured representations of statements can be generated, up to a point, by information extraction systems but these are costly to port to new languages and ...
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Learning semantic features for fMRI data from definitional text
... verbs 1 reflecting, in their words, “basic sensory and motor activities, actions performed on objects, and actions involving changes to spatial relationships”. For each of the 60 nouns corresponding to the stim- ului ...
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QuickSet: Multimodal Interaction for Simulation Set up and Control
... integration agent: The multimodal interpretation agent accepts typed feature structure meaning representations from the language and gesture recognition agents, and produces a unified mu[r] ...
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Adversarial Category Alignment Network for Cross domain Sentiment Classification
... Sentiment classification aims to automatically identify the sentiment polarity (i.e., positive or negative) of the textual data. It has attracted a surge of attention due to its widespread ap- plications, ranging from ...
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A Study on Neural Network in Image Processing
... This report discusses the theory and implementation of an Optical Character Recognition (OCR) for Bangla. The principal idea is to convert images of text documents such as those obtained from scanning a document into ...
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