Hello! 
I am trying to use CorText to extract a list of terms based on their chi2 specificity; however, my text is in a slightly unusual format: each document in my corpus represents a political speech, containing words extracted from a previously completed dictionary analysis. The text in each document therefor consists of a word followed by a period, then another word followed by another period (e.x: “president. help. president. leader. cruel. protect. lawyers. president. cruel. country. president. communities. nation. help.”), making each document essentially a string of one-word sentences. 
I have tried extracting terms based on sentence level co-occurrences, but the resulting “extracted terms” list does not have the “Specificity Chi2”, “Gf-idf”, “Occurrences”, and “Co-occurrences” columns I am used to seeing when I have used Cortext in the past (instead there is just a new column called “ngram log-likelihood). Is the extraction working properly? My goal is to extract a list of terms with the highest chi2 specificity based on the number of times they occur in each document in my corpus (*not* the number of distinct documents they occur in, which I believe the document-level analysis would provide). In other words, I want a ranking of these words which gives a lower weight to words used frequently by all documents in my corpus, and a higher weight to words used frequently in fewer documents etc. 
Let me know if there is a way I can achieve this! 
Thanks so much! 
Kobi Hackenburg