2,977 research outputs found
Learning to Associate Words and Images Using a Large-scale Graph
We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201
Detecting and Monitoring Hate Speech in Twitter
Social Media are sensors in the real world that can be used to measure the pulse of societies.
However, the massive and unfiltered feed of messages posted in social media is a phenomenon that
nowadays raises social alarms, especially when these messages contain hate speech targeted to a
specific individual or group. In this context, governments and non-governmental organizations
(NGOs) are concerned about the possible negative impact that these messages can have on individuals
or on the society. In this paper, we present HaterNet, an intelligent system currently being used by
the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that
identifies and monitors the evolution of hate speech in Twitter. The contributions of this research
are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social
network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on
hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification
approaches based on different document representation strategies and text classification models. (4)
The best approach consists of a combination of a LTSM+MLP neural network that takes as input the
tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area
under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the
literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation
grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
Evaluating the readability of a text can significantly facilitate the precise
expression of information in a written form. The formulation of text
readability assessment demands the identification of meaningful properties of
the text and correct conversion of features to the right readability level.
Sophisticated features and models are being used to evaluate the
comprehensibility of texts accurately. Still, these models are challenging to
implement, heavily language-dependent, and do not perform well on short texts.
Deep reinforcement learning models are demonstrated to be helpful in further
improvement of state-of-the-art text readability assessment models. The main
contributions of the proposed approach are the automation of feature
extraction, loosening the tight language dependency of text readability
assessment task, and efficient use of text by finding the minimum portion of a
text required to assess its readability. The experiments on Weebit, Cambridge
Exams, and Persian readability datasets display the model's state-of-the-art
precision, efficiency, and the capability to be applied to other languages.Comment: 8 pages, 2 figures, 6 equations, 7 table
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