Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Kate Niederhoffer, Kristy Hollingshead, Philip Resnik, Rebecca Resnik, Kate Loveys (Editors)


Anthology ID:
W19-30
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-30
DOI:
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PDF:
https://aclanthology.org/W19-30.pdf

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Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
Kate Niederhoffer | Kristy Hollingshead | Philip Resnik | Rebecca Resnik | Kate Loveys

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Identifying therapist conversational actions across diverse psychotherapeutic approaches
Fei-Tzin Lee | Derrick Hull | Jacob Levine | Bonnie Ray | Kathy McKeown

While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational action each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.

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CLaC at CLPsych 2019 : Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online PostsCLaC at CLPsych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts
Elham Mohammadi | Hessam Amini | Leila Kosseim

This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).

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Suicide Risk Assessment with Multi-level Dual-Context Language and BERTBERT
Matthew Matero | Akash Idnani | Youngseo Son | Salvatore Giorgi | Huy Vu | Mohammad Zamani | Parth Limbachiya | Sharath Chandra Guntuku | H. Andrew Schwartz

Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with any-risk, personality factors from the non-suicide contexts provide distinction of the levels of risk : low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.

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Using natural conversations to classify autism with limited data : Age matters
Michael Hauser | Evangelos Sariyanidi | Birkan Tunc | Casey Zampella | Edward Brodkin | Robert Schultz | Julia Parish-Morris

Spoken language ability is highly heterogeneous in Autism Spectrum Disorder (ASD), which complicates efforts to identify linguistic markers for use in diagnostic classification, clinical characterization, and for research and clinical outcome measurement. Machine learning techniques that harness the power of multivariate statistics and non-linear data analysis hold promise for modeling this heterogeneity, but many models require enormous datasets, which are unavailable for most psychiatric conditions (including ASD). In lieu of such datasets, good models can still be built by leveraging domain knowledge. In this study, we compare two machine learning approaches : the first approach incorporates prior knowledge about language variation across middle childhood, adolescence, and adulthood to classify 6-minute naturalistic conversation samples from 140 age- and IQ-matched participants (81 with ASD), while the other approach treats all ages the same. We found that individual age-informed models were significantly more accurate than a single model tasked with building a common algorithm across age groups. Furthermore, predictive linguistic features differed significantly by age group, confirming the importance of considering age-related changes in language use when classifying ASD. Our results suggest that limitations imposed by heterogeneity inherent to ASD and from developmental change with age can be (at least partially) overcome using domain knowledge, such as understanding spoken language development from childhood through adulthood.

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The importance of sharing patient-generated clinical speech and language data
Kathleen C. Fraser | Nicklas Linz | Hali Lindsay | Alexandra König

Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.

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Depressed Individuals Use Negative Self-Focused Language When Recalling Recent Interactions with Close Romantic Partners but Not Family or FriendsFriends
Taleen Nalabandian | Molly Ireland

Depression is characterized by a self-focused negative attentional bias, which is often reflected in everyday language use. In a prospective writing study, we explored whether the association between depressive symptoms and negative, self-focused language varies across social contexts. College students (N = 243) wrote about a recent interaction with a person they care deeply about. Depression symptoms positively correlated with negative emotion words and first-person singular pronouns (or negative self-focus) when writing about a recent interaction with romantic partners or, to a lesser extent, friends, but not family members. The pattern of results was more pronounced when participants perceived greater self-other overlap (i.e., interpersonal closeness) with their romantic partner. Findings regarding how the linguistic profile of depression differs by type of relationship may inform more effective methods of clinical diagnosis and treatment.

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Semantic Characteristics of Schizophrenic Speech
Kfir Bar | Vered Zilberstein | Ido Ziv | Heli Baram | Nachum Dershowitz | Samuel Itzikowitz | Eiran Vadim Harel

Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.

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Mental Health Surveillance over Social Media with Digital Cohorts
Silvio Amir | Mark Dredze | John W. Ayers

The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.

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Analyzing the use of existing systems for the CLPsych 2019 Shared TaskCLPsych 2019 Shared Task
Alejandro González Hevia | Rebeca Cerezo Menéndez | Daniel Gayo-Avello

In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.

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Similar Minds Post Alike : Assessment of Suicide Risk Using a Hybrid Model
Lushi Chen | Abeer Aldayel | Nikolay Bogoychev | Tao Gong

This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models : a behaviour model ; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features : posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his / her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.

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Suicide Risk Assessment on Social Media : USI-UPF at the CLPsych 2019 Shared TaskUSI-UPF at the CLPsych 2019 Shared Task
Esteban Ríssola | Diana Ramírez-Cifuentes | Ana Freire | Fabio Crestani

This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users’ posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures.

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An Investigation of Deep Learning Systems for Suicide Risk Assessment
Michelle Morales | Prajjalita Dey | Thomas Theisen | Danny Belitz | Natalia Chernova

This work presents the systems explored as part of the CLPsych 2019 Shared Task. More specifically, this work explores the promise of deep learning systems for suicide risk assessment.