Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Tokenization, parsing, classical NLP pipelines, translation and information extraction.
Signals: nlp, natural-language-processing, tokenizer, named-entity-recognition, text-classification, machine-translation, sentiment-analysis, spacy
635 results
Parser Building Toolkit for JavaScript
Portfolio of capstone projects from AI & Technology, Data Science, AI Internship, Machine Learning and AI Humanities Honors. CNN pneumonia detection on chest X-rays (>90% accuracy), healthcare EDA, diabetes risk prediction and AI ethics research. Python, scikit-learn, TensorFlow/Keras.
Final Year Fake News Detection using Machine learning Project with Report, PPT, Code, Research Paper, Documents and Video Explanation.
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
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].
使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.
Simple Keras model that tackles the Stanford Natural Language Inference (SNLI) corpus using summation and/or recurrent neural networks
[ICLR 2021] Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (https://arxiv.org/abs/2010.13993)
A neural network architecture for NLP tasks, using cython for fast performance. Currently, it can perform POS tagging, SRL and dependency parsing.
Translating Akkadian signs to transcriptions using NLP techniques such as HMM, MEMM and BiLSTM neural networks.
A natural language classifier using Node Natural with a BrainJS neural network
Using sparse coding to find distributed representations used by neural networks.
Implementation of character based convolutional neural network
Implementation of topic models based on neural network approaches.
Topic-Aware Convolutional Neural Networks for Extreme Summarization
Neural network toolkit for sentence pair modeling.
Recurrent Neural Network that predicts word-by-word
Neural Network for generating structured queries from natural language.
Neural network sequence labeling model
Text classification using a convolutional neural network.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (IJCAI 2019)
Ranking Sentences for Extractive Summarization with Reinforcement Learning
CIKM2023 Best Demo Paper Award. HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊
Pretrain and finetune ELECTRA with fastai and huggingface. (Results of the paper replicated !)
24,545 repositories in the index in total.
