293 skills found · Page 1 of 10
csurfer / Rake NltkPython implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
parulnith / Building A Simple Chatbot In Python Using NLTKBuilding a Simple Chatbot from Scratch in Python (using NLTK)
hb20007 / Hands On Nltk TutorialThe hands-on NLTK tutorial for NLP in Python
vgel / Summarize.pyA python script for summarizing articles using nltk
amberkakkar01 / Health Care ChatbotIt is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.
javedsha / Text ClassificationMachine Learning and NLP: Text Classification using python, scikit-learn and NLTK
janlukasschroeder / Nlp Cheat Sheet PythonNLP Cheat Sheet, Python, spacy, LexNPL, NLTK, tokenization, stemming, sentence detection, named entity recognition
DadaNanjesha / AI Text Humanizer AppTransform AI-generated text into formal, human-like, and academic writing with ease, avoids AI detector!
PythonProgramming / NLTK 3 Natural Language Processing With Python SeriesNatural Language Processing with Python 3 and NLTK 3 series
hukenovs / Hh ResearchАвтоматизация поиска и исследования вакансий с сайта hh.ru (Headhunter) с помощью методов Python. Классификация данных, поиск статистических параметров.
yohasebe / LemmatizerLemmatizer for text in English. Inspired by Python's nltk.corpus.reader.wordnet.morphy
abhishek305 / PyBot A ChatBot For Answering Python Queries Using NLPPybot 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.
kevinschaich / Billboard🎤 Lyrics/associated NLP data for Billboard's Top 100, 1950-2015.
yonycherkos / Applied Data Science With Python SpecializationThe 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
zhuyuanxiang / NLTK Python CN创建《Python自然语言处理》学习代码的中文注释版本。
KKshitiz / J.A.R.V.I.SIron man inspired Personal virtual assistant
nagypeterjob / Sentiment Analysis NLTK ML LSTMSentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.
TGDivy / MBTI Personality ClassifierA model which uses your social media posting predict your MBTI personality type.
KushalVijay / Natural Language Processing Zero To HeroStep by step road map to learn and understand Natural Language Processing
apanimesh061 / VaderSentimentJavaJava port of Python NLTK Vader Sentiment Analyzer. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.
