Sluice NetworksCode for Sluice networks: Learning what to share between loosely related tasks
Stars: ✭ 135 (-14.56%)
Nlp PapersPapers and Book to look at when starting NLP 📚
Stars: ✭ 111 (-29.75%)
Speech signal processing and classificationFront-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].
Stars: ✭ 155 (-1.9%)
Chars2vecCharacter-based word embeddings model based on RNN for handling real world texts
Stars: ✭ 130 (-17.72%)
Nl2sql阿里天池首届中文NL2SQL挑战赛top6
Stars: ✭ 146 (-7.59%)
MedquadMedical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites
Stars: ✭ 129 (-18.35%)
PostaggaA Library to parse natural language in pure Clojure and ClojureScript
Stars: ✭ 152 (-3.8%)
Deep LyricsLyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network
Stars: ✭ 127 (-19.62%)
Neuro🔮 Neuro.js is machine learning library for building AI assistants and chat-bots (WIP).
Stars: ✭ 126 (-20.25%)
SlingSLING - A natural language frame semantics parser
Stars: ✭ 1,892 (+1097.47%)
KeitaMy personal toolkit for PyTorch development.
Stars: ✭ 124 (-21.52%)
Awesome Nlp ResourcesThis repository contains landmark research papers in Natural Language Processing that came out in this century.
Stars: ✭ 145 (-8.23%)
Dan Jurafsky Chris Manning NlpMy solution to the Natural Language Processing course made by Dan Jurafsky, Chris Manning in Winter 2012.
Stars: ✭ 124 (-21.52%)
ChineseblueChinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE)
Stars: ✭ 149 (-5.7%)
Multihead Siamese NetsImplementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.
Stars: ✭ 144 (-8.86%)
Spacy Js🎀 JavaScript API for spaCy with Python REST API
Stars: ✭ 123 (-22.15%)
Monkeylearn PythonOfficial Python client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Python apps.
Stars: ✭ 143 (-9.49%)
Nlp Pretrained ModelA collection of Natural language processing pre-trained models.
Stars: ✭ 122 (-22.78%)
Spacymoji💙 Emoji handling and meta data for spaCy with custom extension attributes
Stars: ✭ 151 (-4.43%)
Tod BertPre-Trained Models for ToD-BERT
Stars: ✭ 143 (-9.49%)
Awesome Nlp📖 A curated list of resources dedicated to Natural Language Processing (NLP)
Stars: ✭ 12,626 (+7891.14%)
DiscobertCode for paper "Discourse-Aware Neural Extractive Text Summarization" (ACL20)
Stars: ✭ 120 (-24.05%)
Paper Survey📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese
Stars: ✭ 140 (-11.39%)
PytextrankPython implementation of TextRank for phrase extraction and summarization of text documents
Stars: ✭ 1,675 (+960.13%)
Spacy Course👩🏫 Advanced NLP with spaCy: A free online course
Stars: ✭ 1,920 (+1115.19%)
Practical Machine Learning With PythonMaster 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.
Stars: ✭ 1,868 (+1082.28%)
Stanford Tensorflow TutorialsThis repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
Stars: ✭ 10,098 (+6291.14%)
XiocExtract indicators of compromise from text, including "escaped" ones.
Stars: ✭ 148 (-6.33%)
TmtoolkitText Mining and Topic Modeling Toolkit for Python with parallel processing power
Stars: ✭ 135 (-14.56%)
Learn To Select DataCode for Learning to select data for transfer learning with Bayesian Optimization
Stars: ✭ 140 (-11.39%)
Unified SummarizationOfficial codes for the paper: A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss.
Stars: ✭ 114 (-27.85%)
Tensorflow NlpNLP and Text Generation Experiments in TensorFlow 2.x / 1.x
Stars: ✭ 1,487 (+841.14%)
ProsodyHelsinki Prosody Corpus and A System for Predicting Prosodic Prominence from Text
Stars: ✭ 139 (-12.03%)
Lingopackage lingo provides the data structures and algorithms required for natural language processing
Stars: ✭ 113 (-28.48%)
Visdial RlPyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning
Stars: ✭ 157 (-0.63%)
Colibri CoreColibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.
Stars: ✭ 112 (-29.11%)
NcrfppNCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
Stars: ✭ 1,767 (+1018.35%)
Opus MtOpen neural machine translation models and web services
Stars: ✭ 111 (-29.75%)
NegapojiJapanese negative positive classification.日本語文書のネガポジを判定。
Stars: ✭ 148 (-6.33%)
DanlpDaNLP is a repository for Natural Language Processing resources for the Danish Language.
Stars: ✭ 111 (-29.75%)
Rasa💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Stars: ✭ 13,219 (+8266.46%)
Detecting Scientific ClaimExtracting scientific claims from biomedical abstracts (powered by AllenNLP), demo:
Stars: ✭ 109 (-31.01%)
Awesome Embedding ModelsA curated list of awesome embedding models tutorials, projects and communities.
Stars: ✭ 1,486 (+840.51%)
Tree TransformerImplementation of the paper Tree Transformer
Stars: ✭ 148 (-6.33%)
Cocoaai🤖 The Cocoa Artificial Intelligence Lab
Stars: ✭ 134 (-15.19%)
Zamia AiFree and open source A.I. system based on Python, TensorFlow and Prolog.
Stars: ✭ 133 (-15.82%)
Mams For AbsaA Multi-Aspect Multi-Sentiment Dataset for aspect-based sentiment analysis.
Stars: ✭ 135 (-14.56%)