Displaying 1 to 9 from 9 results

flashtext - Extract Keywords from sentence or Replace keywords in sentences.

  •    Python

This module can be used to replace keywords in sentences or extract keywords from sentences. It is based on the FlashText algorithm. Documentation can be found at FlashText Read the Docs.

rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.

  •    Python

RAKE short for Rapid Automatic Keyword Extraction algorithm, is a domain independent keyword extraction algorithm which tries to determine key phrases in a body of text by analyzing the frequency of word appearance and its co-occurance with other words in the text. If you see a stopwords error, it means that you do not have the corpus stopwords downloaded from NLTK. You can download it using command below.

retext-keywords - plugin to extract keywords and key-phrases

  •    Javascript

retext plugin to extract keywords and key-phrases. This package is ESM only: Node 12+ is needed to use it and it must be imported instead of required.

Crackr - Keyword Extraction system using Brown Clustering - (This version is trained to extract keywords from job listings)

  •    Python

Keyword extraction is an extremely interesting topic in Information Retrieval- keywords are widely acknowledged to be extremely important in the field of text retrieval, and particularly while developing large scale modern search engines that limit the size of the inverted index used by the system. In this project we propose to build a system using modern NLP techniques such as Part of Speech Tagging, Brown Clustering and Rapid Automatic Keywords Extraction (RAKE) to use a small initial seed of keywords to generate more candidate keywords in a semi-supervised manner and expose the system as a JSON based web service.

abbreviation-extraction - Python3 implementation of the Schwartz-Hearst algorithm for extracting abbreviation-definition pairs

  •    Python

This is a Python3 implementation of the Schwartz-Hearst algorithm for identifying abbreviations and their corresponding definitions in free text[1]. I have taken the liberty of taking Vincent's code, simplifying it a little, refactoring it for Python 3, and adding some tests.

text-summarization-and-visualization-using-watson-studio - Can we quickly summarize & visualize text to get the details about the unstructured data? Yes we can! Please review this code pattern for all the steps involved to quickly summarize & visualize the data

  •    Jupyter

We will demonstrate a methodology to summarize & visualize text using Watson Studio. Text summarization is the process of creating a short and coherent version of a longer document. There are two methods to summarize the text, extractive & abstractive summarization. We will focus on extractive summarization which involves the selection of phrases and sentences from the source document to make up the new summary. Techniques involve ranking the relevance of phrases in order to choose only those most relevant to the meaning of the source. Some of the advantages of text summarization are below. We will also demonstrate different methods to visualize the data which can aid in providing quick peek of the data. Summaries reduce reading time. When researching documents, summaries make the selection process easier.Text summarization improves the effectiveness of indexing.Text summarization algorithms are less biased than human summarizers. Personalized summaries are useful in question-answering systems as they provide personalized information.Using automatic or semi-automatic summarization systems enables commercial abstract services to increase the number of texts they are able to process.

OpnEco - OpnEco is a Python3 project developed to aid content writers throughout the content writing process

  •    HTML

OpnEco is a Python3 project developed just for that. To be a constant companion throughout your content writing process. By content writers, for content writers.

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