Mar 12, 2020 Google Key phrase Planner can generate key phrase recommendations from a. (excessive vary)” exhibits the upper vary of what advertisers have traditionally paid for a key phrase’s high of web page bid. Then it’s most likely a great key phrase to focus on. Dec 17, 2019 Extract key phrases. The Key Phrase Extraction API extracts key-phrases from a text document, using the Key Phrases method. The following example extracts key phrases for both English and Spanish documents. Create a new Ruby project in your favorite IDE. Add the code provided below. Copy your Text Analytics key and endpoint into the code. Key Phrase Extraction for Question Generation Our work is built upon a family of two-stage generation models that first extract key phrases then generate questions based upon extracted key phrases. Key phrase extraction (KPE) alone is an interesting research question.
- Generate Focus Key Phrase From A Page Book
- Generate Focus Key Phrase From A Page Examples
- Generate Focus Key Phrase From A Page Pdf
- Generate Focus Key Phrase From A Page To Word
- Key Phrase Outline
This article shows you how to detect language, analyze sentiment, extract key phrases, and identify linked entities using the Text Analytics APIs with Ruby.
Tip
For detailed API technical documentation and to see it in action, use the following links. You can also send POST requests from the built-in API test console. No setup is required, simply paste your resource key and JSON documents into the request:
- Latest stable API - v2.1
- Latest preview API - v3.0-Preview.1
Prerequisites
A key and endpoint for a Text Analytics resource. Azure Cognitive Services are represented by Azure resources that you subscribe to. Create a resource for Text Analytics using the Azure portal or Azure CLI on your local machine. You can also:
- Get a trial key valid for seven days for free. After signing up, it will be available on the Azure website.
- View your resource on the Azure portal
Detect language
The Language Detection API detects the language of a text document, using the Detect Language method.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Language detection response
A successful response is returned in JSON, as shown in the following example:
Analyze sentiment
The Sentiment Analysis API detects the sentiment of a set of text records, using the Sentiment method. The following example scores two documents, one in English and another in Spanish.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
![Generate Focus Key Phrase From A Page Generate Focus Key Phrase From A Page](https://p7y9z4c4.stackpathcdn.com/wp-content/uploads/2019/03/SEO-focus-keyword-06.png)
Sentiment analysis response
A successful response is returned in JSON, as shown in the following example:
Extract key phrases
The Key Phrase Extraction API extracts key-phrases from a text document, using the Key Phrases method. The following example extracts key phrases for both English and Spanish documents.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Key phrase extraction response
A successful response is returned in JSON, as shown in the following example:
Entity recognition
The Entities API extracts entities in a text document, using the Entities method. The following example identifies entities for English documents.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Entity extraction response
A successful response is returned in JSON, as shown in the following example:
Next steps
See also
Text Analytics overview
Frequently asked questions (FAQ)
Frequently asked questions (FAQ)
Built this package as a toy challenge to do the following:
Generate Focus Key Phrase From A Page Book
1 - Compute the most important key-words (a key-word can be between 1-3 words)
2 - Choose the top n words from the previously generated list. Compare these key- words with all the words occurring in all of the transcripts.
3 - Generate a score (rank) for these top n words based on analysed transcripts.
What this package does:
Generate Focus Key Phrase From A Page Examples
1 - Generates the keywords (from 1-3 words in length) from a document based, based on the RAKE algorithm
2 - Generate vector representations of all key words and words in a test corpus, using Word2Vec.
3 - Ranks key words by comparing key word vectors with paragraph/document vectors from test corpus
4 - Saves ranked keywords to text file (and/or displays on the console)
Installing dependencies
Generate Focus Key Phrase From A Page Pdf
The code was developed with python 3.5 and requires the following libraries/versions:
gensim2.0.0numpy1.12.1scikit-learn0.18.1wget3.2
These dependencies are specified in requirements.txt, and can be downloaded via the following command:
Generate Focus Key Phrase From A Page To Word
Usage
Running the keyword_xtract file, will carry out the steps described above (keyword extraction -> compute vector representations -> rank key words)
Models available:
A truncated version of Google's pre-trained Word2Vec model is available as default. GloVe Word2Vec models (https://nlp.stanford.edu/projects/glove/) can also be downloaded by specifying the model required at run time:
glove_6B - Wikipedia 2014 + Gigaword 5 (6B tokens, 400K vocab, uncased, 50d, 100d, 200d, & 300d vectors, 822 MB download)glove_42B - Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): glove.42B.300d.zipglove_840B - Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zipglove_twitter - Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 25d, 50d, 100d, & 200d vectors, 1.42 GB download)
Use the labels above as inputs for the '-m/--model' command line arguments. If the selected model is not present, the model will be downloaded; this may take some time. It is also possible to use custom user-defined Word2Vec models by supplying a path to the model.
NOTE - the default evaluation docs provided for ranking keywords are 3 document pages related to food, which were extracted from Wikipedia. Please provide your own relevant evaluation documents for accurate keyword ranking. Otherwise, keywords can simply be extracted and the ranking scores ignored.
RAKE algorithm + implementation
![Key phrase outline Key phrase outline](/uploads/1/2/6/0/126045901/573633996.png)
I modified an existing RAKE implementation to work with Python 3 and different parameters. In this implementation, RAKE does the following:
(i) Generate key word candidates(ii) Computes 'scores' for each candidate. Words are scored according to their frequency and the typical length of a candidate phrase in which they appear.
Originally implemented by: https://github.com/aneesha/RAKEForked from: https://github.com/BelalC/RAKE-tutorial/tree/master
A Python implementation of the Rapid Automatic Keyword Extraction (RAKE) algorithm as described in:Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In M. W. Berry & J. Kogan (Eds.), Text Mining: Theory and Applications: John Wiley & Sons.
The source code is released under the MIT License.
Word2Vec + Ranking
Utilising gensim and pre-trained Word2Vec models, keyword vector representations are computed. Vector representations of evaluation documents are computed by taking the average of the word vectors present in a specified document. The pairwise cosine similarity between each keyword vector and evaluation document vector are computed and averaged, giving a single score which can be utilised as a 'rank' for the keyword.
Key Phrase Outline
Gensim - https://radimrehurek.com/gensim/index.htmlVector represenations of words and phrases - Distributed Representations of Words and Phrases and their Compositionality; Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg; Dean, Jeffrey, arXiv:1310.4546