Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression

Mental illnesses such as depression andanxiety are highly prevalent, and therapyis increasingly being offered online. Thisnew setting is a departure from face-to-face therapy, and offers both a challengeand an opportunity – it is not yet knownwhat features or approaches are likely tolead to successful outcomes in such a dif-ferent medium, but online text-based ther-apy provides large amounts of data for lin-guistic analysis. We present an initial in-vestigation into the application of compu-tational linguistic techniques, such as topicand sentiment modelling, to online ther-apy for depression and anxiety. We findthat important measures such as symptomseverity can be predicted with compara-ble accuracy to face-to-face data, usinggeneral features such as discussion topicand sentiment; however, measures of pa-tient progress are captured only by finer-grained lexical features, suggesting thataspects of style or dialogue structure mayalso be important

Investigating Topic Modelling for Therapy Dialogue Analysis

Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalise to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalise and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation.

Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs

INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM with highway layers using words, word probabilities, disfluency features, pause information, and a variety of acoustic features, achieves an accuracy of 84% and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting cognitive decline is more challenging, our models show improvement using the multimodal approach and word probabilities, disfluency and pause information over word-only models. We show considerable gains for AD classification using multimodal fusion and gating, which can effectively deal with noisy inputs from acoustic features and ASR hypotheses

Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model

Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model's outputs

The Use of English Colour Terms in Big Data

This study explores the use of English colour names in large datasets from informal Twitter messages and the well-structured corpus of Google Books. Because colour names in text have no directly associated chromatic stimuli, the corresponding colour categories of colour words was assessed from responses in an online colour naming experiment. A comparison of the frequency in the three datasets revealed that the mapping of colour names to perceptually uniform colour spaces does not reflect natural language colour distributions

Incremental Composition in Distributional Semantics

Despite the incremental nature of Dynamic Syntax (DS), the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal

Creative Language Generation in a Society of Engagement and Reflection

Conference proceeding from ICCC'20 International Conference on Computational Creativity. Many existing models of narrative and language generation use rigid sequences of steps which are cognitively implausible and limit creativity. Iterative models based on Sharples' cycle of engagement and reflection improve on this by incorporating self-evaluation but still have a rigid arrangement of parts. This paper outlines how a multi-agent approach could be used to break apart the cycle into a more fluid society of engagement and reflection, whose constituent agents interact with one another to produce a text. Our approach is to work in a simple domain in order to focus on the underlying processes, and to avoid the Eliza effect during evaluation

Incremental Composition in Distributional Semantics

Despite the incremental nature of Dynamic Syntax (DS), the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal

Single pulse and profile variability study of PSR J1022+1001

Millisecond pulsars (MSPs) are known as highly stable celestial clocks. Nevertheless, recent studies have revealed the unstable nature of their integrated pulse profiles, which may limit the achievable pulsar timing precision. In this paper, we present a case study on the pulse profile variability of PSR J1022+1001. We have detected approximately 14,000 sub-pulses (components of single pulses) in 35-hr long observations, mostly located at the trailing component of the integrated profile. Their flux densities and fractional polarisation suggest that they represent the bright end of the energy distribution in ordinary emission mode and are not giant pulses. The occurrence of sub-pulses from the leading and trailing components of the integrated profile is shown to be correlated. For sub-pulses from the latter, a preferred pulse width of approximately 0.25 ms has been found. Using simultaneous observations from the Effelsberg 100-m telescope and the Westerbork Synthesis Radio Telescope, we have found that the integrated profile varies on a timescale of a few tens of minutes. We show that improper polarisation calibration and diffractive scintillation cannot be the sole reason for the observed instability. In addition, we demonstrate that timing residuals generated from averages of the detected sub-pulses are dominated by phase jitter, and place an upper limit of ~700 ns for jitter noise based on continuous 1-min integrations.Comment: 13 pages, 20 figures, 3 tables, accepted for publication in MNRA