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201 changes: 201 additions & 0 deletions LICENSE
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90 changes: 44 additions & 46 deletions README.md
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# python semantic complexity text analyzer
Contributors: Rémi Venant
# Python semantic complexity text analyzer

pysemcom (pySemanticComplexity) allow to compute from a bunch of different text files lexical,
syntactical and semantic complexity as vectors for each of these texts.
Contributors:
- [Rémi Venant](https://github.com/theDartagnan), main author & developer
- [Aldan Creo](https://github.com/acmcmc), documentation contributions

Semantic complexity relies on DBpedia Entity Recognition Graph computation based on multiple ontologies used in DBPedia.
PyComplex offers several subprograms to process multiples files in parallels.
pysemcom (pySemanticComplexity) allows to compute **vectors** representing lexical, syntactic and **semantic complexity** for a set of given texts.

One of these subprogram is the full pipeline of translation from texts to vectors of semantic complexity.
For each text file processed in parallel, the pipeline first clean and split text in paragraphs,
and identify the DBpedia entity with the use of a Spotlight REST Api. Each entity is then enrich with its types by
queriyng a DBpedia SPARQL endpoint. Each list of entities (for each document) is then processed in parallel to compute
a graph of concept, conposed of the entities, their types and the hierachy of ontology classes that define these types.
Finally each graph is vectorized in parallel. Note that three ontologies are used so far: Schema, DBpedia ontology and
Yago. The result csv file is composed one line per fil, each of them being composed of the file name (without its
extension) and the several complexity features.
The measure of semantic complexity is based on the DBpedia Entity Recognition Graph computation, which is based on several ontologies used in DBPedia.
PyComplex provides several subroutines to process multiple files in parallel.

Most importantly, one of these subprograms is the complete pipeline of translation from texts to vectors of semantic complexity.
For each text file processed in parallel, the pipeline first cleans up the text, splits it into paragraphs, and identifies the DBpedia entity using a Spotlight REST API.
Each entity is then enriched with its types by querying a DBpedia SPARQL endpoint.
Each list of entities (for each document) is then processed in parallel to compute a concept graph composed of the entities, their types, and the hierarchy of ontology classes that define those types.
Finally, each graph is vectorized in parallel.
Note that three ontologies are used so far: Schema, DBpedia and Yago. The resulting `csv` file is composed of one line per file, each having the file name (without extension) and the different complexity features.

## 1. Repository structure
This respository is structed as follow:
This repository is structured as follows:

- pysemcom.py: the main application entrypoint for the analyzer (usable in command line)
- batch, dpedia, utils: the different python packages for the application
- vendor: local resources from other providers. Only the ontologies used in DBpedia so far
- pyComplex: the python packages and programs to compute syntactic, lexical and semantic complexity of a text.
- dbpedia-spotlight-docker: a docker-compose file to manage a DBpedia Spotlight server
- requirements.txt: the python package requirement for the afelTraces2rdf application
- README.md: this file
- `pysemcom.py`: the main application entry point for the analyzer (usable from the command line)
- `batch`, `dpedia`, `utils`: the various python packages for the application
- `vendor`: local resources from other vendors. Only the ontologies used in DBpedia so far
- `pyComplex`: the python packages and programs for calculating the syntactic, lexical and semantic complexity of a text.
- `dbpedia-spotlight-docker': a docker-compose file for managing a DBpedia Spotlight server.
- requirements.txt': the python package requirements for the afelTraces2rdf application

## 2. Requirements
The afelTraces2rdf application relies on the following softwares:
We require `python >= 3.6`.
Optionally, for syntactic and lexical parsing, you can add the Stanford parser (see section 3.1).

- python >= 3.6
- pip
- Stanford parser, tregex, lca & pos tagger (optional, for syntactic and lexical parsing only. See 4.1)
The required packages are listed in `requirements.txt`, and you can install them by running:

The required packages for the application are listed in the requirements.txt file.
An automatic installation of the packages can be achieved by the following command, to be executed within the repository folder:
```
pip install -r requirements.txt
```

pip install -r requirements.txt
__Working within a virtual environment is recommended.__

__Working with a virtual environment is recommended.__
## 3. Setup

## 4. Setup
- Go to `vendor/dbpedia/` and decompress `yago_taxonomy.ttl.bz2` into `yago_taxonomy.ttl` within the same directory
- Install the Python dependencies as described in section 2

- Go to vendor/dbpedia and decompress yago_taxonomy.ttl.bz2 into yago_taxonomy.ttl within the same directory
- Install the python dependencies if it is not already done (see section 2. of this document)

### 4.1 Stanford tool
- Go to the vendor/ directory and create the directory "stanford".
- Go to the stanford/ directory.
### 3.1 Stanford tool
- Go to the `vendor/` directory and create the directory `stanford/`.
- Go to the `stanford/` directory.
- Download the following files within that directory:
- [Lexical Complexity Analyzer](http://www.personal.psu.edu/xxl13/downloads/lca.tgz)
- [Syntactic Complexity Analyzer](http://personal.psu.edu/xxl13/downloads/L2SCA-2016-06-30.tgz)
- [Pos tagger](https://nlp.stanford.edu/software/stanford-postagger-full-2018-02-27.zip)
- Decompress the 3 archives (eg.: tar -xvzf lca.tgz)
- You should have the following sub-directories:
- lca (with at least anc_all_count.txt and bnc_all_count.txt files)
- L2SCA-2016-06-30 (with at least standford-tregex.jar file)
- stanford-parser-full-2014-01-04 (with at least stanford-parser.jar file)
-stanford-postagger-full-2018-02-27 (with at least stanford-postagger.jar file and model dir)
- `lca` (with at least `anc_all_count.txt` and `bnc_all_count.txt` files)
- `L2SCA-2016-06-30` (with at least `standford-tregex.jar` file)
- `stanford-parser-full-2014-01-04` (with at least `stanford-parser.jar` file)
- `stanford-postagger-full-2018-02-27` (with at least `stanford-postagger.jar` file and `model` dir)

## 3. Use of the analyzer
The analyzer can be launched from a terminal. Inside the repository folder, one can run the following command to get the help:
## 4. Use of the analyzer
The analyzer can be launched from a terminal. Inside the repository folder, you can get help by running:

python pysemcom.py --help
```
python pysemcom.py --help
```

## 4. Licence
The python text complexity analyzer is distributed under the [Apache Licence V2](https://www.apache.org/licenses/LICENSE-2.0). Please attribute Rémi Venant through the [AFEL Project](http://afel-project.eu)* when reusing and redistributing this code.
## 5. Licence
This package is distributed under the [Apache Licence V2](https://www.apache.org/licenses/LICENSE-2.0). Please attribute Rémi Venant through the [AFEL Project](http://afel-project.eu) when reusing and redistributing this code.