In comparison to text classification, the typical PIPELINE for IE necessitates more fine-grained NLP processing. For example, we'd need to know the part-of-speech tags of words to identify named entities (people, organisations, etc.). We WOULD require COREFERENCE resolution to connect various references to the same entity (e.g., Albert Einstein, Einstein, the scientist, he, etc.). It's worth noting that none of these stages are required for creating a text classification system. As a result, IE is a more NLP-intensive operation than text categorization. Not all steps in the pipeline are required for all IE jobs, as shown in the diagram, and the figure shows which IE tasks necessitate which degrees of analysis.

Other than named entity recognition, all other IE tasks require deeper NLP pre-processing followed by models developed for those specific tasks. Key phrase extraction is the TASK that requires the least amount of NLP processing (some algorithms also do POS tagging before extracting key phrases), WHEREAS all other IE tasks require deeper NLP pre-processing followed by models developed for those specific tasks. Standard evaluation sets are often used to assess IE tasks in terms of precision, recall, and F1 scores. Because of the various levels of NLP pre-processing required, the accuracy of these processing steps has an impact on IE jobs. All of these factors should be considered when collecting relevant training data and, if necessary, training our own models for IE.