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In research & news articles, keywords form an important component since they provide a concise representation of the article’s content. Keywords also play a crucial role in locating the article from information retrieval systems, bibliographic databases and for search engine optimization. Keywords also help to categorize the article into the relevant subject or discipline.
Conventional approaches of extracting keywords involve manual assignment of keywords based on the article content and the authors’ judgment. This involves a lot of time & effort and also may not be accurate in terms of selecting the appropriate keywords. With the emergence of Natural Language Processing (NLP), keyword extraction has evolved into being effective as well as efficient.
And in this article, we will combine the two — we’ll be applying NLP on a collection of articles (more on this below) to extract keywords.
A brief overview of how to use fastText to train powerful text classifiers in a python notebook. - mpuig/textclassification
TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
Features
- Noun phrase extraction
- Part-of-speech tagging
- Sentiment analysis
- Classification (Naive Bayes, Decision Tree)
- Language translation and detection powered by Google Translate
- Tokenization (splitting text into words and sentences)
- Word and phrase frequencies
- Parsing
- n-grams
- Word inflection (pluralization and singularization) and lemmatization
- Spelling correction
- Add new models or languages through extensions
- WordNet integration
Resumio is a Telegram Bot that creates a summary from a web article. Just copy & paste the url or share the url with Resumio and you will get back a 4-paragraphs summary.
Magpie is a deep learning tool for multi-label text classification. It learns on the training corpus to assign labels to arbitrary text and can be used to predict those labels on unknown data. It has been developed at CERN to assign subject categories to High Energy Physics abstracts and extract keywords from them.
Opensource python library for text mining focusing on document similarity identification.
A iPython notebook that introduces how to use the topicmodels module for implementing Latent Dirichlet Allocation using the collapsed Gibbs sampling algorithm of Griffiths and Steyvers (2004). The module contains three classes: one for processing raw text, another for implementing LDA, and another for querying. This tutorial will go through the main features of each, for full details see the documented source code.