{'fraction_non_alphanumeric': 0.014545454545454545, 'fraction_numerical': 0.0, 'mean_word_length': 5.191011235955056, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Entity Extraction in the Ecological Domain - A practical guide

Scientific information comes in many shapes: As data in databases or spreadsheets, but also as textual information in papers and books. In order to exploit all this information and integrate all the knowledge that is available regarding a specific entity, it is necessary to identify entities and their relationships. In this paper, we provide a guideline to setting up a pipeline that supports entity and relationship extraction from scientific publications from the ecological domain.