897,687 research outputs found
Graph Convolutional Networks for Text Classification
Text classification is an important and classical problem in natural language
processing. There have been a number of studies that applied convolutional
neural networks (convolution on regular grid, e.g., sequence) to
classification. However, only a limited number of studies have explored the
more flexible graph convolutional neural networks (convolution on non-grid,
e.g., arbitrary graph) for the task. In this work, we propose to use graph
convolutional networks for text classification. We build a single text graph
for a corpus based on word co-occurrence and document word relations, then
learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text
GCN is initialized with one-hot representation for word and document, it then
jointly learns the embeddings for both words and documents, as supervised by
the known class labels for documents. Our experimental results on multiple
benchmark datasets demonstrate that a vanilla Text GCN without any external
word embeddings or knowledge outperforms state-of-the-art methods for text
classification. On the other hand, Text GCN also learns predictive word and
document embeddings. In addition, experimental results show that the
improvement of Text GCN over state-of-the-art comparison methods become more
prominent as we lower the percentage of training data, suggesting the
robustness of Text GCN to less training data in text classification.Comment: Accepted by 33rd AAAI Conference on Artificial Intelligence (AAAI
2019
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
Adaptive Region Embedding for Text Classification
Deep learning models such as convolutional neural networks and recurrent
networks are widely applied in text classification. In spite of their great
success, most deep learning models neglect the importance of modeling context
information, which is crucial to understanding texts. In this work, we propose
the Adaptive Region Embedding to learn context representation to improve text
classification. Specifically, a metanetwork is learned to generate a context
matrix for each region, and each word interacts with its corresponding context
matrix to produce the regional representation for further classification.
Compared to previous models that are designed to capture context information,
our model contains less parameters and is more flexible. We extensively
evaluate our method on 8 benchmark datasets for text classification. The
experimental results prove that our method achieves state-of-the-art
performances and effectively avoids word ambiguity.Comment: AAAI 201
IndiText Boost: Text Augmentation for Low Resource India Languages
Text Augmentation is an important task for low-resource languages. It helps
deal with the problem of data scarcity. A data augmentation strategy is used to
deal with the problem of data scarcity. Through the years, much work has been
done on data augmentation for the English language. In contrast, very less work
has been done on Indian languages. This is contrary to the fact that data
augmentation is used to deal with data scarcity. In this work, we focus on
implementing techniques like Easy Data Augmentation, Back Translation,
Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for
text classification on different languages. We focus on 6 Indian languages
namely: Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. According to
our knowledge, no such work exists for text augmentation on Indian languages.
We carry out binary as well as multi-class text classification to make our
results more comparable. We get surprising results as basic data augmentation
techniques surpass LLMs
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
Semantic Learning and Web Image Mining with Image Recognition and Classification
Image mining is more than just an extension of data mining to image domain. Web Image mining is a technique commonly used to extract knowledge directly from images on WWW. Since main targets of conventional Web mining are numerical and textual data, Web mining for image data is on demand. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. This paper proposes a novel image recognition and image classification technique using a large number of images automatically gathered from the Web as learning images. For classification the system uses imagefeature- based search exploited in content-based image retrieval(CBIR), which do not restrict target images unlike conventional image recognition methods and support vector machine(SVM), which is one of the most efficient & widely used statistical method for generic image classification that fit to the learning tasks. By the experiments it is observed that the proposed system outperforms some existing search system
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