Creating Temporal Cohesion with SpaCy

Sebastian Duerr

NLP and Machine Learning Engineer, Persuaide

Sebastian Duerr is a Machine Learning Scholar at MIT in Boston, focusing on Natural Language Processing and Generation. Furthermore, he collaborates with to integrate NLP deep learning solutions into their service offer.

Sebastian Duerr
Sebastian Duerr
Session description

One of many tedious text manipulation tasks involves the consistent alteration of tense to either past, present, or future. McNamara et al. (2013) confer that temporal cohesion (i.e., using a consistent tense throughout a text) increases readability and comprehension. Let us tackle this computationally!

In this hands-on workshop, we are going to address the task of consistently altering tense in texts with the go-to NLP library SpaCy.

(Source: McNamara et al. 2013, Natural language processing in an intelligent writing strategy tutoring system, Behavior Research Methods.)