semvec/pypi-packages huggingface dataset:
neutron-beam 19.3.1
Client to beam files to and from neutron64.com
Keywords: development

# Neutron Beam

Neutron Beam streams files to and from the Neutron64.com code editor. It can
be used locally and remotely. It also comes built in with a web console shell 
that integrates with Neutron64.com

See https://www.neutron64.com for more info.
semcluster 0.0.1
Unsupervised automatic keyphrase extraction

# SemCluster
 Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation

### Installation

1. Clone this Repo and its submodules using
    ```bash
    git clone --recurse-submodules git@code.siemens.com:palle.klewitz/semcluster.git
    ```

2. Install esa-wiki
    ```bash
    cd ESA-Wiki
    python3 setup.py sdist bdist_wheel
    pip3 install dist/wiki_esa-0.0.1-py3-none-any.whl
    cd ..
    ```

3. Install semcluster
    ```bash
    python3 setup.py sdist bdist_wheel
    pip3 install dist/semcluster-0.0.1-py3-none-any.whl
    ```

### Usage

```python
from semcluster import SemClusterKeywordExtractor

text = "The quick brown fox jumps over the lazy dog."

# Instantiate extractor
e = SemClusterKeywordExtractor()

# Extract phrases
keyphrases = e.extract_keyphrases(text, seed_threshold=0.7, verbose=True)
```
esa-wiki 0.0.1
Explicit Semantic Analysis

# ESA-Wiki
Explicit Semantic Analysis based on Wikipedia

This is a python library which contains code to 1) construct a semantic interpreter based on data from Wikipedia and 2) apply this to various kinds of texts.

To construct an interpreter, first obtain a Wikipedia XML dump from http://dumps.wikimedia.org/enwiki/

1) Then run `python3 -m esa_wiki.xml_parse <file>` with the downloaded file as its argument. This outputs some temporary files containing information on the words, links and articles encountered.

2) Next, run `python3 -m esa_wiki.generate_indices` to generate lists of indices corresponding to unique words and articles encountered

3) Finally, run `python3 -m esa_wiki.matrix_builder` to construct a very large sparse interpretation matrix. Each row corresponds to a unique word, each column to a 'concept', i.e. a Wikipedia article, and each entry is the TF-IDF score for word i in article j. The Matrix is saved in separate chunks to conserve memory.

medium_wiki.xml can be used as an example file for demonstration/testing purposes, as it contains only the first 100 or so Wikipedia articles.

cunning_linguistics.py then contains classes to perform text analysis and harvest tweets for analysis.