PHOX_config.ini configures the initial settings for PHOX pipeline and should be included in the working directory.
[Server] server_name = <server name for http: site> username = <user name for ftp login to server_name> password = <user password for ftp login to server_name> server_dir = <path to directory on the server where subdirectories are located> [Pipeline] scraper_stem = <stem for scrapped output> recordfile_stem = <stem for output of monger_formatter.py> fullfile_stem = <stem for output of TABARI.0.8.4b1> eventfile_stem = <stem for event output of oneaday_formatter.py> dupfile_stem = <stem for duplicate file output of oneaday_formatter.py> outputfile_stem = <stem for files uploaded by phox_uploader.py>
Example of PHOX_config.ini
[Server] server_name = openeventdata.org username = myusername password = myweakpassword12345 server_dir = public_html/datasets/phoenix/ [Pipeline] scraper_stem = scraper_results_20 recordfile_stem = eventrecords. fullfile_stem = events.full. eventfile_stem = Phoenix.events. dupfile_stem = Phoenix.dupindex. outputfile_stem = Phoenix.events.20
It is now possible to code event data from a limited list of sources that is different from that used within the web scraper. For instance, it might be desirable to scrape content from a wide variety of sources, but some of this content may be too noisy to include in an event dataset or there is some experementation necessary to determine which sources to include in a final dataset. The data sources are restricted using the source_keys.txt file. These keys correspond to those found in the source field within the MongoDB instance created by the web scraper.
PETRARCH (Python Engine for Text Resolution And Related Coding Hierarchy) is an event coding program used to machine code even data from formatted source texts in the pipeline. PETRARCH is the next-generation replacement for the TABARI event data coding software. PETRARCH is dictionary-based and relies on a full parse generated by natural language processing software such as Stanford’s CoreNLP along with pattern recognition to identify ‘who-did-what-to-whom’ relations.