182 Open source projects that are alternatives of or similar to nlp-akash

A small NLP SAAS project that summarize a webpage

TextRank implementation for Python 3.

Gazeta: Dataset for automatic summarization of Russian news / Газета: набор данных для автоматического реферирования на русском языке

No description or website provided.

[AAAI2021] Unsupervised Opinion Summarization with Content Planning

Python wrapper for evaluating summarization quality by ROUGE package

A tool to automatically summarize documents abstractively using the BART or PreSumm Machine Learning Model.

Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.

[NAACL2018] Entity Commonsense Representation for Neural Abstractive Summarization

Implementation of a seq2seq model for summarization of textual data. Demonstrated on amazon reviews, github issues and news articles.

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.

Dynamic Entity Summarization (DynES)

A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.

Lecture Notes of Andrew Ng's Machine Learning Course

An NLP tool which classifies and summarizes resumes

Notetaking Electron app that can answer your questions and makes summaries for you

An affect generator based on TextBlob and the NRC affect lexicon. Note that lexicon license is for research purposes only.

reimplementing Neural Summarization by Extracting Sentences and Words

NLP with NLTK for Sentiment analysis amazon Products Reviews

Source code for our "TitleStylist" paper at ACL 2020

paribhasha.herokuapp.com/

Code for EMNLP 2021 paper "Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization"

(COLING'18) The source code for the paper "Structure-Infused Copy Mechanisms for Abstractive Summarization".

An implementation of transformer-based language model for sentence rewriting tasks such as summarization, simplification, and grammatical error correction.

US Bill Summarization Corpus

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

FactSumm: Factual Consistency Scorer for Abstractive Summarization

A bidirectional encoder-decoder LSTM neural network is trained for text summarization on the cnn/dailymail dataset. (MIT808 project)

Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

A module for E-mail Summarization which uses clustering of skip-thought sentence embeddings.

자연어 처리와 텍스트 분석을 위한 오픈소스 파이썬 라이브러리 입니다.

Extracts key terminology (n-grams) from any large collection of documents (>1000) and forecasts emergence

📹 A tool for automatic video creation and uploading on YouTube

Natural Language Processing using NLTK and Spacy

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 T…

[EMNLP 2019] Mixture Content Selection for Diverse Sequence Generation (Question Generation / Abstractive Summarization)

Extracts character names from a text file and performs analysis of text sentences containing the names.

A well-structured summarization dataset for the Persian language!

API server for NLTK

Code for "Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization"

Few-shot learning framework for opinion summarization published at EMNLP 2020.

Statistical and Semantical Text Summarizer in Persian Language

Python AI assistant 🧠

keras project that parses and analyze english resumes

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

NLP Cheat Sheet, Python, spacy, LexNPL, NLTK, tokenization, stemming, sentence detection, named entity recognition

A SVM model that classifies the reviews as real or fake. Used both the review text and the additional features contained in the data set to build a model that predicted with over 85% accuracy without using any deep learning techniques.

Awesome-Text-Classification Projects,Papers,Tutorial .

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].

My solutions to the projects assigned for the Udacity Computer Vision Nanodegree

Creating a software for automatic monitoring in online proctoring

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.

Multilingual automatic text summarizer using statistical approach and extraction

An introduction to Natural Language processing using NLTK with python.

A company/project name generator for Python. Uses NLTK and diverse techniques derived from existing corporate etymologies and naming agencies for sophisticated word generation and ideation.

A python chatbot framework with Natural Language Understanding and Artificial Intelligence.

NLTK Source

The pytorch implementation of paper "DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization"

Ruby port of the NLTK Punkt sentence segmentation algorithm