Metadata-Version: 2.1
Name: implicit-word-network
Version: 0.0.2
Summary: A python package for extracting and exploring context-enriched word networks from corpora
Home-page: https://gitlab.inf.uni-konstanz.de/julian.schelb/implicit-word-network
Author: Julian Schelb
Author-email: julian.schelb@uni-konstanz.de
License: UNKNOWN
Project-URL: Bug Tracker, https://gitlab.inf.uni-konstanz.de/julian.schelb/implicit-word-network/-/issues
Description: # Implicit Word Network
        
        ## Introduction
        This python package can be used to extract context-enriched implicit word networks as described by Spitz and Gertz. The theoretical background is explained in the following publications:
        
           1. Spitz, A. (2019). Implicit Entity Networks: A Versatile Document Model. Heidelberg University Library. https://doi.org/10.11588/HEIDOK.00026328
           2. Spitz, A., & Gertz, M. (2018). Exploring Entity-centric Networks in Entangled News Streams. In Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. Companion of the The Web Conference 2018. ACM Press. https://doi.org/10.1145/3184558.3188726
        
        ## Dependencies
        
        This project uses models from the spaCy and sentence_transformers package. These packages are not installed automatically. You can use the following commands to install them.
        
        ```console
        pip install sentence_transformers
        pip install spacy
        python -m spacy download en_core_web_sm
        ```
        
        ## Example Usage
        
        ```python
        
        import spacy as sp
        import implicit_word_network as wn
        
        # Path to text file
        path = "data.txt"
        
        # Entities to search for in corpus
        entity_types = ["PERSON", "LOC", "NORP", "ORG", "WORK_OF_ART"]
        
        c = 2  # Cut-off parameter
        
        # Importing data ...
        D = wn.readDocuments(path)
        
        # Parsing data ...
        nlp = sp.load("en_core_web_sm")
        D_parsed = wn.parseDocuments(D, entity_types, nlp=nlp)
        
        # Converting parsing results ...
        D_mat = wn.createCorpMat(D_parsed)
        
        # Building graph ...
        V, Ep = wn.buildGraph(D_mat, c)
        
        ```
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
