自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构，中文分词，词性标注，命名实体识别，新词发现，关键词，文本摘要，文本相似度，科学计算器，中文数字阿拉伯数字(罗马数字)转换，中文繁简转换，拼音转换。tookit(tool) of NLP，CWS(chinese word segnment)，POS(Part-Of-Speech Tagging)，NER(name entity recognition)，Find(new words discovery)，Keyword(keyword extraction)，Summarize(text summarization)，Sim(text similarity)，Calculate(scientific calculator)，Chi2num(chinese number to arabic number)

:mortar_board:RESEARCH [NLP :speech_balloon:] This is an implementation of "Automatic Consensus-Based Text Summarizer" along with text-organizing capabilities that can generate genre-specific, generic or user-configured summaries of a large amount of unorganized text. We are currently using a number of independent text-mining algorithms based on different statistical models to compute the summaries and combining them using configurable consensus techniques.:exclamation::boom:

Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, and more...

A simple Flask website for all NLP tasks which includes Text Preprocessing, Keyword Extraction, Text Summarization etc. Created Date: 30 Jan 2019

This project extracts audio from YouTube videos, converts speech to text using OpenAI Whisper, and summarizes the content using transformer-based NLP models to save time and improve content accessibility.

Bangla NLP dataset. Bangla NER,POStag, text summarization, stopword, translate, sentiment analysis, wiki articles, root word, dataset etc.

Lightweight NLP text summarizer using Streamlit and TF-IDF. Summarize long texts instantly!

AI Emotional Mirror is an AI/ML project that analyzes free-form text to summarize thoughts, detect emotions, and generate calm, non-judgmental emotional reflections using NLP, transformers, and generative AI.

URL articles text summarizer using Web Crawling and NLP (written in Python)

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, and much more...

The Research Paper Summary Project automates the summarization of research papers using Python and Natural Language Processing (NLP). It extracts key information, generates concise summaries, identifies keywords, and formats citations in various styles. The project uses libraries like NLTK and BeautifulSoup for text processing and fetching papers.

documentiq is an open-source intelligent text processor by GEOSOFT. It analyzes, summarizes, paraphrases, and enhances text and PDFs using NLP and AI models. Built for students, researchers, and writers who need clarity, brevity, and correctness in one place.

Online Inference API for NLP Transformer models - summarization, text classification, sentiment analysis and more

Text Summarization using NLP to fetch BBC News Article and summarize its text and also it includes custom article Summarization

:mortar_board:RESEARCH [NLP] Analysis of N-gram Graphs and their applications in the domain of Text Classification and Extraction based Summarization

The Summarizer from the Web IR / NLP Group (WING), hence SWING, is a modular, state-of-the-art automatic extractive text summarization system. It is used as the basis for summarization research at the National University of Singapore. It performs as one of the leading automatic summarization systems in the international TAC competition, getting high marks for the ROUGE evaluation measure

Contains different course tutorials and jupyter notebook file for applying different Deep Learning models in different NLP tasks such as text classification, summarization, translation, etc.

A super fast walkthrough of NLP Text Summarization with Hugging Face Transformers.

Advanced NLP Workshop: word-sense disambiguation with RoBERTa and text summarization with BART (Machine Learning Milan)

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, and much more...

:globe_with_meridians: :memo: An NLP powered Google Chrome extension to summarize, paraphrase, get named entities, and find keyword synonyms from highlighted text.

Text Summarization on Spotify Podcast Transcripts for NLP class at @UNIBO

We will build a Flask web app that can input any long piece of information such as a blog or news article and summarize it into just five lines! Text summarization is an NLP(Natural Language Processing) task. SBERT(Sentence-BERT) has been used to achieve the same.

A text summarization tool for Marathi implemented as a project for course Adavanced NLP (CSCI 544)

#Assignment Answers #About this Specialization: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

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Discussion Summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.

Computer Vision and NLP based document scanner, text extractor and summarizer.

OCR Process: Extracting text from PDF with TesseractTable Recognition: Detecting tables with YOLOv8 and saving in CSV format NLP Analysis: Summarizing text and sentiment analysis with BERT Smart Search: Finding answers to questions in the document with LangChain Output Management: Saving text and table outputs to the outputs folder