# Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy

arXiv: https://arxiv.org/abs/1809.00640 · HTML: https://mlsanity.com/paper/1809.00640/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations](https://mlsanity.com/paper/2102.11573/) (2021)
- [Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies](https://mlsanity.com/paper/2102.11265/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [A Survey of Cognitive Distortion Detection and Classification in NLP](https://mlsanity.com/paper/2508.09878/) (2025)
- [GDPR Compliant Collection of Therapist-Patient-Dialogues](https://mlsanity.com/paper/2211.12360/) (2022)
- [Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation](https://mlsanity.com/paper/2511.01482/) (2025)
- [Multi-Perspective Triad Interaction Graph Neural Network for Cognitive Distortion Detection](https://mlsanity.com/paper/2608.06785/) (2026)

## Abstract (arXiv)

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.