jgtextrank | jgtextrank : Yet another Python implementation | Natural Language Processing library
kandi X-RAY | jgtextrank Summary
kandi X-RAY | jgtextrank Summary
This is a parallelisable and highly customisable implementation of the TextRank algorithm [Mihalcea et al., 2004]. You can define your own co-occurrence context, syntactic categories(choose either "closed" filters or "open" filters), stop words, feed your own pre-segmented/pre-tagged data, and many more. You can also load co-occurrence graph directly from your text for visual analytics, debug and fine-tuning your custom settings. This implementation can also be applied to large corpus for terminology extraction. It can be applied to short text for supervised learning in order to provide more interesting features than conventional TF-IDF Vectorizer. TextRank algorithm look into the structure of word co-occurrence networks, where nodes are word types and edges are word cooccurrence. Important words can be thought of as being endorsed by other words, and this leads to an interesting phenomenon. Words that are most important, viz. keywords, emerge as the most central words in the resulting network, with high degree and PageRank. The final important step is post-filtering. Extracted phrases are disambiguated and normalized for morpho-syntactic variations and lexical synonymy (Csomai and Mihalcea 2007). Adjacent words are also sometimes collapsed into phrases, for a more readable output. Mihalcea, R., & Tarau, P. (2004, July). TextRank: Bringing order into texts. Association for Computational Linguistics.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Extract keywords extraction from a corpus directory
- Extract keywords from a segmented corpus
- Validate the weight_comb option
- Check if export option is valid
- Evaluate the test set
- Loads a set of GULth3GS terms
- Concatenate a sequence of ngrams
- Return raw data from file
- Evaluate ACL1 dataset
- Extract keywords from the tagged corpus
- Prints the results of the objective function
- Loads preprocessed corpus context
- Evaluate the genia dataset
- Load GENIA GLS term list from file
- Normalize synonym normalization
- Load a list of GO Terms from a file
- Evaluate a testset
- Loads a science evaluation dataset
- Extract keywords from a text file
- Read file contents
- Compute a vertex from the given syntactic unit corpus
- Finds all cooccur words in the given context
- Calculate the term term and final score
- Returns a list of punctuation tokens that are punctuation
- Calculate the GC value of a candidate term
- Return a list of POS tagging
jgtextrank Key Features
jgtextrank Examples and Code Snippets
Community Discussions
Trending Discussions on jgtextrank
QUESTION
I have problem to setup my project in Travis CI python 3.7 environment, although simply running with 'pip install scipy' in python 3.6 works fine. There lots of similar/same problems and solutions reported before [1], but answers does not work for me.
Can anyone help to explain why it works with python 3.6 [2], while failed with python 3.7 in Travis CI server ?
I finally tried with the following scripts, but it still does not work in Travis CI (python 3.7) env. Travis CI (python 3.6 and python 3.6 dev) still work fine.
...ANSWER
Answered 2018-Jan-01 at 19:42There are no binary wheels yet for Python 3.7 (the list is on https://pypi.python.org/pypi/scipy) so pip downloads the source package for SciPy and attempts to compile it. This fails most of the time because you need a full development environment for Python and the assorted libraries (BLAS and LAPACK with development headers), see https://scipy.org/install.html and https://github.com/scipy/scipy/blob/master/INSTALL.rst.txt .
Your solutions:
- Roll back to Python 3.6, which I would suggest.
- Make sure to install the development libraries for Python 3.7 and the libraries (+ headers) for BLAS and LAPACK.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install jgtextrank
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page