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nltk / nltk

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Failing tests in DocStrings

ekaf
ekaf commented May 1, 2022

Checking the Python files in NLTK with "python -m doctest" reveals that many tests are failing. In many cases, the failures are just cosmetic discrepancies between the expected and the actual output, such as missing a blank line, or unescaped linebreaks. Other cases may be real bugs.

If these failures could be avoided, it would become possible to improve CI by running "python -m doctest" each t

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good first issue tests CI
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dipanjanS / practical-machine-learning-with-python

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Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

python machine-learning natural-language-processing computer-vision deep-learning jupyter notebook clustering tensorflow scikit-learn keras jupyter-notebook pandas spacy nltk classification convolutional-neural-networks prophet statsmodels time-series-analysis
  • Updated Oct 1, 2020
  • Jupyter Notebook
text-analytics-with-python

dipanjanS / text-analytics-with-python

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Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

python semantic natural-language-processing sentiment-analysis text-classification clustering pattern natural-language scikit-learn sentiment spacy nltk text-summarization gensim stanford-nlp text-analytics
  • Updated Dec 24, 2020
  • Jupyter Notebook

gionanide / Speech_Signal_Processing_and_Classification

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Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

nlp classifier natural-language-processing feature-extraction nltk gaussian-mixture-models support-vector-machines mfcc principal-component-analysis speech-processing linear-discriminant-analysis isomap spectral-clustering long-short-term-memory kernel-pca spectral-embedding locally-linear-embedding linear-prediction-coefficients speech-utterance
  • Updated Dec 6, 2021
  • Python
nlp_workshop_odsc_europe20

dipanjanS / nlp_workshop_odsc_europe20

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Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

python machine-learning natural-language-processing deep-learning tensorflow scikit-learn jupyter-notebook transformers pytorch spacy nltk gensim transfer-learning
  • Updated Sep 18, 2020
  • Jupyter Notebook

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