62,445 research outputs found
Image-based Text Classification using 2D Convolutional Neural Networks
We propose a new approach to text classification
in which we consider the input text as an image and apply
2D Convolutional Neural Networks to learn the local and
global semantics of the sentences from the variations of the
visual patterns of words. Our approach demonstrates that
it is possible to get semantically meaningful features from
images with text without using optical character recognition
and sequential processing pipelines, techniques that traditional
natural language processing algorithms require. To validate
our approach, we present results for two applications: text
classification and dialog modeling. Using a 2D Convolutional
Neural Network, we were able to outperform the state-ofart
accuracy results for a Chinese text classification task and
achieved promising results for seven English text classification
tasks. Furthermore, our approach outperformed the memory
networks without match types when using out of vocabulary
entities from Task 4 of the bAbI dialog dataset
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Many important forms of data are stored digitally in XML format. Errors can
occur in the textual content of the data in the fields of the XML. Fixing these
errors manually is time-consuming and expensive, especially for large amounts
of data. There is increasing interest in the research, development, and use of
automated techniques for assisting with data cleaning. Electronic dictionaries
are an important form of data frequently stored in XML format that frequently
have errors introduced through a mixture of manual typographical entry errors
and optical character recognition errors. In this paper we describe methods for
flagging statistical anomalies as likely errors in electronic dictionaries
stored in XML format. We describe six systems based on different sources of
information. The systems detect errors using various signals in the data
including uncommon characters, text length, character-based language models,
word-based language models, tied-field length ratios, and tied-field
transliteration models. Four of the systems detect errors based on expectations
automatically inferred from content within elements of a single field type. We
call these single-field systems. Two of the systems detect errors based on
correspondence expectations automatically inferred from content within elements
of multiple related field types. We call these tied-field systems. For each
system, we provide an intuitive analysis of the type of error that it is
successful at detecting. Finally, we describe two larger-scale evaluations
using crowdsourcing with Amazon's Mechanical Turk platform and using the
annotations of a domain expert. The evaluations consistently show that the
systems are useful for improving the efficiency with which errors in XML
electronic dictionaries can be detected.Comment: 8 pages, 4 figures, 5 tables; published in Proceedings of the 2016
IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna
Hills, CA, USA, pages 79-86, February 201
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