1,383 research outputs found
Document image classification combining textual and visual features.
This research contributes to the problem of classifying document images. The main addition of this thesis is the exploitation of textual and visual features through an approach that uses Convolutional Neural Networks.
The study uses a combination of Optical Character Recognition and Natural Language Processing algorithms to extract and manipulate relevant text concepts from document images.
Such content information are embedded within document images, with the aim of adding elements which help to improve the classification results of a Convolutional Neural Network.
The experimental phase proves that the overall document classification accuracy of a Convolutional Neural Network trained using these text-augmented document images, is considerably higher than the one achieved by a similar model trained solely on classic document images, especially when different classes of documents share similar visual characteristics. The comparison between our method and state-of-the-art approaches demonstrates the effectiveness of combining visual and textual features.
Although this thesis is about document image classification, the idea of using textual and visual features is not restricted to this context and comes from the observation that textual and visual information are complementary and synergetic in many aspects
Learning and evaluating the content and structure of a term taxonomy
Journal ArticleIn this paper, we describe a weakly supervised bootstrapping algorithm that reads Web texts and learns taxonomy terms. The bootstrapping algorithm starts with two seed words (a seed hypernym (Root concept) and a seed hyponym) that are inserted into a doubly anchored hyponym pattern. In alternating rounds, the algorithm learns new hyponym terms and new hypernym terms that are subordinate to the Root concept. We conducted an extensive evaluation with human annotators to evaluate the learned hyponym and hypernym terms for two categories: animals and people
Once Upon a Text: an ANT Tale in Text Analysis
International audienc
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
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