164,781 research outputs found
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa
A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language
Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS. We have also provided a comparison of our model with a state of the art method
used for the same purpos
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which
provides the unique possibility to analyze the relative aggregation/folding
propensity of the elements of the entire Escherichia coli (E. coli) proteome in
a cell-free standardized microenvironment. The hardness of the problem comes
from the superposition between the driving forces of intra- and inter-molecule
interactions and it is mirrored by the evidences of shift from folding to
aggregation phenotypes by single-point mutations [10]. Here we apply several
state-of-the-art classification methods coming from the field of structural
pattern recognition, with the aim to compare different representations of the
same proteins gathered from the Niwa et al. data base; such representations
include sequences and labeled (contact) graphs enriched with chemico-physical
attributes. By this comparison, we are able to identify also some interesting
general properties of proteins. Notably, (i) we suggest a threshold around 250
residues discriminating "easily foldable" from "hardly foldable" molecules
consistent with other independent experiments, and (ii) we highlight the
relevance of contact graph spectra for folding behavior discrimination and
characterization of the E. coli solubility data. The soundness of the
experimental results presented in this paper is proved by the statistically
relevant relationships discovered among the chemico-physical description of
proteins and the developed cost matrix of substitution used in the various
discrimination systems.Comment: 17 pages, 3 figures, 46 reference
A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy
Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409
Deep Learning for the Radiographic Detection of periodontal Bone Loss
We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies
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