478,682 research outputs found
Vowel classification based approach for Telugu Text-to-Speech System using symbol concatenation
Telugu is one of the oldest languages in India. This paper describes the development of Telugu Text-to-Speech System (TTS) using vowel classification. Vowels are most important class of sound in most Indian languages. The duration of vowel is longer than consonants and is most significant. Here vowels are categorized as starting middle and end according to the position of occurrence in a word. The algorithm developed by us involves analysis of a sentence in terms of words and then symbols involving combination of pure consonants and vowels. Wave files are being merged as per the requirement to generate the modified consonants influenced by deergalu (vowel sign) and yuktaksharas generate the speech from a text. Speech unit database consisting of vowels (starting, middle and end) and consonants is developed. We evaluated our TTS using Mean Opinion Score (MOS) for intelligibility and voice quality with and without using vowel classification from sixty five listeners, and got better results with vowel classification
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Most existing zero-shot learning methods consider the problem as a visual
semantic embedding one. Given the demonstrated capability of Generative
Adversarial Networks(GANs) to generate images, we instead leverage GANs to
imagine unseen categories from text descriptions and hence recognize novel
classes with no examples being seen. Specifically, we propose a simple yet
effective generative model that takes as input noisy text descriptions about an
unseen class (e.g.Wikipedia articles) and generates synthesized visual features
for this class. With added pseudo data, zero-shot learning is naturally
converted to a traditional classification problem. Additionally, to preserve
the inter-class discrimination of the generated features, a visual pivot
regularization is proposed as an explicit supervision. Unlike previous methods
using complex engineered regularizers, our approach can suppress the noise well
without additional regularization. Empirically, we show that our method
consistently outperforms the state of the art on the largest available
benchmarks on Text-based Zero-shot Learning.Comment: To appear in CVPR1
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Data augmentation is a widely used technique to address the problem of text
classification when there is a limited amount of training data. Recent work
often tackles this problem using large language models (LLMs) like GPT3 that
can generate new examples given already available ones. In this work, we
propose a method to generate more helpful augmented data by utilizing the LLM's
abilities to follow instructions and perform few-shot classifications. Our
specific PromptMix method consists of two steps: 1) generate challenging text
augmentations near class boundaries; however, generating borderline examples
increases the risk of false positives in the dataset, so we 2) relabel the text
augmentations using a prompting-based LLM classifier to enhance the correctness
of labels in the generated data. We evaluate the proposed method in challenging
2-shot and zero-shot settings on four text classification datasets: Banking77,
TREC6, Subjectivity (SUBJ), and Twitter Complaints. Our experiments show that
generating and, crucially, relabeling borderline examples facilitates the
transfer of knowledge of a massive LLM like GPT3.5-turbo into smaller and
cheaper classifiers like DistilBERT and BERT. Furthermore,
2-shot PromptMix outperforms multiple 5-shot data augmentation methods on the
four datasets. Our code is available at
https://github.com/ServiceNow/PromptMix-EMNLP-2023.Comment: Accepted to EMNLP 2023 (Long paper
Generating Counterfactual Explanations with Natural Language
Natural language explanations of deep neural network decisions provide an
intuitive way for a AI agent to articulate a reasoning process. Current textual
explanations learn to discuss class discriminative features in an image.
However, it is also helpful to understand which attributes might change a
classification decision if present in an image (e.g., "This is not a Scarlet
Tanager because it does not have black wings.") We call such textual
explanations counterfactual explanations, and propose an intuitive method to
generate counterfactual explanations by inspecting which evidence in an input
is missing, but might contribute to a different classification decision if
present in the image. To demonstrate our method we consider a fine-grained
image classification task in which we take as input an image and a
counterfactual class and output text which explains why the image does not
belong to a counterfactual class. We then analyze our generated counterfactual
explanations both qualitatively and quantitatively using proposed automatic
metrics.Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2018), Stockholm, Swede
Web Mediators for Accessible Browsing
We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. K-means clustering is used to create unique thresholds to differentiate index pages and article pages on individual web sites. Index pages contain mostly links to articles and other indices, while article pages contain mostly text. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface, which shows that we can vastly improve the rate of communication for users with disabilities. We used web page classification and link grouping to alter web page display on an accessible web browser that we developed to make a usable browsing interface for users with disabilities. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier.National Science Foundation (IIS-0308213, IIS-039009, IIS-0093367, P200A01031, EIA-0202067
A Novel Approach in Feature Selection Method for Text Document Classification
In this paper, a novel approach is proposed for extract eminence features for classifier. Instead of traditional feature selection techniques used for text document classification. We introduce a new model based on probability and over all class frequency of term. We applied this new technique to extract features from training text documents to generate training set for machine learning. Using these machine learning training set to automatic classify documents into corresponding class labels and improve the classification accuracy. The results on these proposed feature selection method illustrates that the proposed method performs much better than traditional methods.
DOI: 10.17762/ijritcc2321-8169.15075
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