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)

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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

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

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.

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

Deploy PhoBERT for Abstractive Text Summarization as REST API using StreamLit, Transformers by Hugging Face and PyTorch

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...

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)

Three modules of extractive text summarization, including implementation of Kmeans clustering using BERT sentence embedding

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.

old school NLP text summarization in .net

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Text summarization refers to the technique of shortening long pieces of text. The intention is to create a coherent and fluent summary having only the main points outlined in the document. Automatic text summarization is a common problem in machine learning and natural language processing (NLP).Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.

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...

NLP projects including sentiment analysis, text summarization, and chatbot development using deep learning and transformers.

An AI-powered system for extracting and summarizing key legal information from complex legal documents using advanced Natural Language Processing (NLP) techniques. This project utilizes SpaCy for preprocessing and entity extraction, and Sumy for text summarization, to generate concise summaries of lengthy legal texts.

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...

A Streamlit application that does NLP tasks such as Sentiment Analysis, Named Entity Recognition (NER), and Text Summarization.