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- Jun 26, 2019 It’s best to use the exact keyword IF you can make the title sound natural (eg. The Ideal W3 Total Cache Settings). Other times it makes more sense to break your keyword up so the headline sounds nice (if my keyword is Yoast Focus Keywords a nice title would be How To Choose Focus Keywords In Yoast).
- Mar 12, 2020 Keyword Phrase Basics. Selecting the focus keyword for your page is a strategic first step for search engine optimization (SEO). The keyword or key phrase for your page helps you focus your writing so it remains concise and on topic. What words would you use to describe you page or post? It is good to be specific.
- Mar 12, 2020 Keyword Phrase Basics. Selecting the focus keyword for your page is a strategic first step for search engine optimization (SEO). The keyword or key phrase for your page helps you focus your writing so it remains concise and on topic. What words would you use to describe you page or post? It is good to be specific.
Built this package as a toy challenge to do the following:
Generate Focus Key Phrase From A Page Pdf
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. 8 proposed an encoder-decoder gen.
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.
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3 - Generate a score (rank) for these top n words based on analysed transcripts.
What this package does:
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.
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3 - Ranks key words by comparing key word vectors with paragraph/document vectors from test corpus
![Generate Generate](https://venturebeat.com/wp-content/uploads/2019/12/WI_gif4.gif?w=800)
4 - Saves ranked keywords to text file (and/or displays on the console)
Installing dependencies
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:
Usage
Running the keyword_xtract file, will carry out the steps described above (keyword extraction -> compute vector representations -> rank key words)
Models available:
Generate Focus Key Phrase From A Page Examples
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
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.
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