Detecting defense mechanisms from Adult Attachment Interview (AAI) transcripts using machine learning.

OBJECTIVE: Defensive functioning (i.e., unconscious process used to manage real or perceived threats) may play a role in the development of various psychopathologies. It is typically assessed via observer rating measures, however, human coding of defensive functioning is resource-intensive and time-consuming. The purpose of this study was to develop a machine learning approach to automate coding of defense mechanisms from interview transcripts. METHOD: Participants included a clinical sample of women with binge-eating disorder (n = 92) and a community sample without binge-eating disorder (n = 66). We trained and evaluated five RoBERTa-based models to detect the presence of defenses in 16,785 interviewer-participant talk-turn pairs nested within 192 interviews. A model detected the presence of any defense, while four additional models detected the most common defenses in this sample (repression, intellectualization, reaction formation, undoing). RESULTS: The models were capable of distinguishing defenses (ROC-AUC .82-.90) but were not proficient enough to warrant replacing human coders (PR-AUC .28-.60). Follow-up analysis was performed to assess other practical uses of these models. DISCUSSION: Our machine learning models could be used to assist coders. Future research should conduct a deployment study to determine if human coding of defense mechanisms can be expedited using machine learning models.

Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A case study.

Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3's understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means.