10,516 research outputs found
Cross-language Text Classification with Convolutional Neural Networks From Scratch
Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach
Fine-graind Image Classification via Combining Vision and Language
Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obtain the semantic parts for better classification
accuracy. Despite achieving promising results, these methods mainly have two
limitations: (1) not all the parts which obtained through the part detection
models are beneficial and indispensable for classification, and (2)
fine-grained image classification requires more detailed visual descriptions
which could not be provided by the part locations or attribute annotations. For
addressing the above two limitations, this paper proposes the two-stream model
combining vision and language (CVL) for learning latent semantic
representations. The vision stream learns deep representations from the
original visual information via deep convolutional neural network. The language
stream utilizes the natural language descriptions which could point out the
discriminative parts or characteristics for each image, and provides a flexible
and compact way of encoding the salient visual aspects for distinguishing
sub-categories. Since the two streams are complementary, combining the two
streams can further achieves better classification accuracy. Comparing with 12
state-of-the-art methods on the widely used CUB-200-2011 dataset for
fine-grained image classification, the experimental results demonstrate our CVL
approach achieves the best performance.Comment: 9 pages, to appear in CVPR 201
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.Comment: CIKM 2018 Full Pape
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents
Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used
word embeddings. Despite the popularity of these techniques, the precise
mechanisms by which they acquire new semantic relations between words remain
unclear. In the present article we investigate whether LSA and Word2vec
capacity to identify relevant semantic dimensions increases with size of
corpus. One intuitive hypothesis is that the capacity to identify relevant
dimensions should increase as the amount of data increases. However, if corpus
size grow in topics which are not specific to the domain of interest, signal to
noise ratio may weaken. Here we set to examine and distinguish these
alternative hypothesis. To investigate the effect of corpus specificity and
size in word-embeddings we study two ways for progressive elimination of
documents: the elimination of random documents vs. the elimination of documents
unrelated to a specific task. We show that Word2vec can take advantage of all
the documents, obtaining its best performance when it is trained with the whole
corpus. On the contrary, the specialization (removal of out-of-domain
documents) of the training corpus, accompanied by a decrease of dimensionality,
can increase LSA word-representation quality while speeding up the processing
time. Furthermore, we show that the specialization without the decrease in LSA
dimensionality can produce a strong performance reduction in specific tasks.
From a cognitive-modeling point of view, we point out that LSA's word-knowledge
acquisitions may not be efficiently exploiting higher-order co-occurrences and
global relations, whereas Word2vec does
Classifying Amharic News Text Using Self-Organizing Maps
The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field.
The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it
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