Natural Language Processing
Alm, Cecilia O. And Alex Hedges. "Visualizing NLP in Undergraduate Students' Learning about Natural Language." Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence (EAAI). Ed. Association for the Advancement of Artificial Intelligence. Virtual, Virtual: AAAI Press, 2021. Web.
Hassan, Saad, Matt Huenerfauth, and Cecilia O. Alm. "Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens." Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021. Ed. Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih. Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021. Web.
Kaushik, Nikhil, et al. "Eliciting Confusion in Online Conversational Tasks." Proceedings of the Proceedings of the 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (at ACII 2021). Ed. NA. Nara, Japan (Online): IEEE, 2021. Web.
Akhbardeh, Farhad, et al. "Handling Extreme Class Imbalance in Technical Logbook Datasets." Proceedings of the Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Long Papers). Ed. Chengqing Zong, et al. Online, Online: Association for Computational Linguistics, 2021. Web.
Maus, Natalie, et al. "Gaze-guided magnification for individuals with vision impairments." Proceedings of the Proceedings of Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. Ed. CHI (Late Breaking Works). Honolulu, HI, USA: ACM, 2020. Web.
Forman, Cleo, et al. "Capturing laughter and smiles under genuine amusement vs. Negative emotion." Proceedings of the Proceedings of the Second Workshop on Human-Centered Computational Sensing (at PerCom 2020). Ed. Amy L. Murphy and Edison Thomaz. Austin, TX, USA [Virtual]: IEEE, 2020. Web.
Kvist, Jonathan, et al. "Dynamic visualization system for gaze and dialogue data." Proceedings of the Proceedings of the Fourth International Conference on Human Computer Interaction Theory and Applications HUCAPP 2020. Ed. Manuela Chessa, Alexis Paljic, Jose Braz. Valetta, Malta: SciTePress, 2020. Web.
Bohlin, Gustaf, et al. "Considerations for Face-based Data Estimates: Affect Reactions to Videos." Proceedings of the Third International Conference on Human Computer Interaction Theory and Applications. Ed. Manuela Chessa, Alexis Paljic, and Jose Braz. Prague, Czech Republic: SCITEPRESS, 2019. Web.
Kafle, Sushant, Cecilia O. Alm, and Matt Huenerfauth. "Modeling Acoustic-prosodic Cues for Word Importance Prediction in Spoken Dialogues." Proceedings of the 8th Workshop on Speech and Language Processing for Assistive Technologies, SLPAT 2019 (at NAACL 2019). Ed. Manuela Chessa, Alexis Paljic, and Jose Braz. Minnesota, MS: ACL, 2019. Web.
Kessler, Lucas, Cecilia O. Alm, and Reynold Bailey. "Synthesized Spoken Names: Biases Impacting Perception." Proceedings of the Interspeech (Show and Tell). Ed. Pejman Mowlaee, Mario Huemer, and Philipp Salletmayr. Graz, Austria: n.P., 2019. Web.
Kafle, Sushant, Cecilia O. Alm, and Matt Huenerfauth. "Fusion Strategy for Prosodic and Lexical Representations of Word Importance." Proceedings of the Interspeech. Ed. Thomas Hain and Björn Schuller. Graz, Austria: ISCA, 2019. Web.
Saraf, Monali, et al. "Multimodal Anticipated Versus Actual Perceptual Reactions." Proceedings of the ICMI19: Adjunct of the 21st ACM International Conference on Multimodal Interaction, Suzhou, China). Ed. Wen Gao, et al. Suzhou, China: ACM, 2019. Web.
Wang, Regina, et al. "Fusing Dialogue and Gaze from Discussions of 2D and 3D Scenes." Proceedings of the ICM19: Adjunct of the 21st ACM International Conference on Multimodal Interaction. Ed. Wen Gao, et al. Suzhou, China: ACM, 2019. Web.
Lucas, Elizabeth, Cecilia O. Alm, and Reynold Bailey. "Understanding Human and Predictive Moderation of Online Science Discourse." Proceedings of the 2019 IEEE Western New York Image and Signal Processing Workshop (WNYISPW). Ed. Raymond Ptucha. Rochester, New York: IEEE, 2019. Web.
Haduong, Nikita, et al. "Multimodal Alignment for Affective Content." Proceedings of the Workshop of Affective Content Analysis (at AAAI 2018). Ed. Niyati Chhaya, Kokil Jaidka, and Lyle H. Ungar. New Orleans, North America: Association for the Advancement of Artificial Intelligence, 2018. Web.
Medina, Rebecca, et al. "Sensing Behaviors of Students in Online vs. Face-to-face Lecturing Contexts." Proceedings of the Workshop on Homan-Centered Computational Sensing (at PerCom 2018). Ed. Cecilia O. Alm, Reynold Bailey, and Ehsan Hoque. Athens, Greece: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, 2018. Web.
Diaz, Yancarlos, et al. "Towards an Affective Video Recommendation System." Proceedings of the Workshop on Homan-Centered Computational Sensing (at PerCom 2018). Ed. Cecilia O. Alm, Reynold Bailey, and Ehsan Hoque. Athens, Greece: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, 2018. Web.
Tornblad, McKenna K., et al. "Sensing and Learning Human Annotators Engaged in Narrative Sensemaking." Proceedings of the Student Research Workshop (at NAACL 2018). Ed. Silvio Ricardo Cordeiro, Shereen Oraby, Umashanthi Pavalanathan, and Kyeongmin Rim. New Orleans, Louisiana: Association for Computational Linguistics, 2018. Web.
Meyers, Benjamin S, et al. "A Dataset for Identifying Actionable Feedback in Collaborative Software Development." Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Ed. Iryna Gurevych and Yusuke Miyao. Melbourne, Australia: Association for Computational Linguistics, 2018. Web.
Vaidyanathan, Preethi, et al. "SNAG: Spoken Narratives and Gaze Dataset." Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Ed. Iryna Gurevych and Yusuke Miyao. Melbourne, Australia: Association for Computational Linguistics, 2018. Web.
Shea, Jordan E., Cecilia O. Alm, and Reynold Bailey. "Contemporary Multimodal Data Collection Methodology for Reliable Inference of Authentic Surprise." Proceedings of the 2018 IEEE Western New York Image and Signal Processing Workshop (WNYISPW). Ed. Ray Ptucha. Rochester, New York: IEEE, 2018. Web.
Gangji, Aliya, Trevor Walden, Preethi Vaidyanathan, Emily Prud'hommeaux, Reynold Bailey, and Cecilia O. Alm. "Using co-captured face, gaze and verbal reactions to images of varying emotional content for analysis and semantic alignment." Proceedings of the Workshop on Human-Aware Artificial Intelligence (at AAAI 2017). Ed. Kartik Talamadupula, et al. San Francisco, CA: AAAI, 2017. Web.
Edwards, Ashley, Anthony Massicci, Srinivas Sridharan, Joe Geigel, Linwei Wang, Reynold Bailey, and Cecilia O. Alm. "Sensor-based methodological observations for studying online learning." Proceedings of the ACM Workshop on Intelligent Interfaces for Ubiquitous and Smart Learning (at IUI 2017). Ed. Ilknur Celik and Ilaria Torre. Limassol, Cyprus, Cyprus: ACM, 2017. Web.
Calderwood, Alexander, Elizabeth A. Pruett, Raymond Ptucha, Christopher M. Homan, Cecilia O. Alm. "Understanding the semantics of narratives of interpersonal violence through reader annotations and physiological reactions." Proceedings of the Workshop on Computational Semantics beyond Events and Roles (at EACL 2017). Ed. Eduardo Blanco, Roser Morante, and Roser Saurí. Valencia, Spain: ACL, 2017. Web.
Munaiah, Nuthan, Benjamin S. Meyers, Cecilia O. Alm, Andrew Meneely, Pradeep K. Murukannaiah. Emily Prud'hommeaux, Josephine Wolff, and Yang Yu. "Natural language insights from code reviews that missed a vulnerability: A large scale study of Chromium." Proceedings of the International Symposium on Engineering Secure Software and Systems. Ed. Eric Bodden, Mathias Payer, Elias Athanasopoulos. Bonn, Germany: n.P., 2017. Web.
Alm, Cecilia O., Benjamin S. Meyers, and Emily Prud'hommeaux. "An analysis and visualization tool for case study learning of linguistics concepts." Proceedings of the Conference on Empirical Methods in Natural Language Processing (Demo papers). Ed. Lucia Specia, Matt Post, Michael Paul. Copenhagen, Denmark: ACL, 2017. Web.
Alm, Cecilia O. And Reynold Bailey. "Team-based, transdisciplinary, and inclusive practices for undergraduate research." Proceedings of the IEEE Frontiers in Education Conference. Ed. N.P. Indianapolis, IN: IEEE, 2017. Web.
Terkik, Andamlak, Emily Prud'hommeaux, Cecilia O. Alm, Christopher Homan, Scott Franklin,. "Analyzing Gender Bias in Student Evaluations." Proceedings of the COLING, the 26th International Conference on Computational Linguistics: Technical Papers. Ed. Nicoletta Calzolari et al. Osaka, Japan: n.P., 2016. Web.
Liu, Tong, Christopher Homan, Cecilia O. Alm, Megan Lytle, Ann Marie White, Henry Kautz,. "Understanding Discourse on Work and Job-related Well-being in Public Social Media." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Ed. Antal van den Bosch et al. Berlin, Germany: n.P., 2016. Web.
Bullard, Joseph, Cecilia O. Alm, Xumin Liu, Ruben A. Proano, Qi Yu, . "Towards Early Dementia Detection: Fusing Linguistic and Non-linguistic Clinical Data." Proceedings of the Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (at NAACL 2016). Ed. Kristy Hollingshead and Lyle Ungar. San Diego, USA: n.P., 2016. Web.
Oak, Mayuresh, Anil K. Behera, Titus Thomas, Cecilia O. Alm, Emily Prud'hommeaux, Christopher M. Homan, Ray Ptucha,. "Generating Clinically Relevant Texts: A Case Study on Life-changing Events." Proceedings of the Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (at NAACL-HLT 2016). Ed. Kristy Hollingshead and Lyle Ungar. San Diego, California: n.P., 2016. Web.
Vaidyanathan, Preethi, Jeff B. Pelz, Emily Prud'hommeaux, Cecilia O. Alm, Anne R. Haake,. "Fusing Eye Movements and Observer Narratives for Expert-driven Image-region Annotations." Proceedings of the ETRA. Ed. Pernilla Qvarfordt and Dan Witzner Hansen. Charleston, SC: n.P., 2016. Web.
Schrading, Nicholas, et al. "An Analysis of Domestic Abuse Discourse on Reddit." Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2577-2583. Ed. Lluís Màrquez, Chris Callison-Burch, Jian Su. Lisbon, Portugal: ACL, 2015. Web.
Vaidyanathan,, Preethi, et al. "Computational Integration of Human Vision and Natural Language Through Bitext Alignment." Proceedings of the Workshop on Vision and Language at the Conference on Empirical Methods in Natural Language Processing, pages 4-5. Ed. --. Lisbon, Portugal: ACL, 2015. Web.
Schrading, Nicholas, et al. "#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse." Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics-Human Language Technologies, pages 1281-1286. Ed. --. Denver, CO: ACL, 2015. Web.
Bethamcherla, Vasudev, et al. "Face-speech Sensor Fusion for Non-invasive Stress Detection." Proceedings of the 1st Joint Conference on Facial Analysis, Animation, and Auditory-Visual Speech Processing, pages 196-201. Ed. --. Vienna, Austria: ISCA, 2015. Web.
Paul, Will, et al. "Stressed out: What Speech Tells us About Stress." Proceedings of the Interspeech 2015, pages 3710-3714. Ed. --. Dresden, Germany: ISCA, 2015. Web.
Vaidyanathan, Preethi, et al. "Alignment of Eye Movements and Spoken Language for Semantic Image Understanding." Proceedings of the 11th International Conference on Computational Semantics, pages 76-81. Ed. --. London, UK: ACL, 2015. Web.
Bullard, Joseph, et al. "Inference from Structured and Unstructured Electronic Medical Data for Dementia Detection." Proceedings of the Operation Research and Computing: Algorithms and Software for Analytics, 14th INFORMS Computing Society Conference (ICS2015), pages 236—244. Ed. --. Richmond, VA: n.P., 2015. Web.
Guo, Xuan, et al. "Fusing Multimodal Human Expert Data Towards Semantic Image Use." Proceedings of the 7th Workshop on Eye Gaze in Intelligent Human Machine Interaction: Eye-Gaze & Multimodality. Ed. Nb. Istanbul, Turkey: ACM, 2014. Web.
Hochberg, Limor, et al. "Towards Automatic Annotation of Clinical Decision-making Style." Proceedings of the LAW VIII - The 8th Linguistic Annotation Workshop at COLING 2014. Ed. Lori Levin, Manfred Stede. Dublin, Ireland: n.P., 2014. Web.
Bullard, Joseph, et al. "Towards Multimodal Modeling of Physicians Diagnostic Confidence and Self-awareness Using Medical Narratives." Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Ed. Junichi Tsujii and Jan Hajic. Dublin, Ireland: Dublin City University and Association for Computational Linguistics, 2014. Web.
Moseley, Nathaniel, Cecilia O. Alm, and Manjeet Rege. "Toward Inferring the Age of Twitter Users From Their Use of Nonstandard Abbreviations and Lexicon." Proceedings of the IEEE IRI 2014. Ed. Elisa Bertino, Bhavani Thuraisingham, Ling Liu, James Joshi. San Francisco,, California: IEEE, 2014. Web.
Hochberg, Limor, Cecilia O. Alm, Esa M. Rantanen, Caroline M. DeLong, and Anne R. Haake. "Decision Style in a Clinical Reasoning Corpus." Proceedings of the 2014 Workshop on Biomedical Natural Language Processing (BioNLP 2014),. Ed. Kevin Bretonnel Cohen et al. Baltimore, Maryland: ACL, Web.
Christopher, Homan, et al. "Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale." Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality at the 52nd Annual Meeting of the Association for Computational Linguistics. Ed. Resnik, P., Resnik, R., Mitchell, M. Baltimore, Maryland: n.P., Web.
Moseley, Nathaniel, Cecilia O. Alm, and Manjeet Rege. "User-annotated Microtext Data for Modeling and Analyzing Sociolinguistic Characteristics and Age Grading." Proceedings of the IEEE Eighth International Conference on Research Challenges in Information Science. Ed. Rebecca Deneckere, Marko Bajec, Martine Collard. Marrakesh, Morocco: IEEE, Web.
Guo, Xuan, et al. "Infusing Perceptual Expertise and Domain Knowledge into a Human-centered Image Retrieval System: A Prototype Application." Proceedings of the ETRA 2014. Ed. Nb. Safety Harbor,, Florida: ACM, Web.
Vaidyanathan, Preethi, et al. "Recurrence Quantification Analysis Reveals Differences in Eye-movement Behavior Between Expert and Novice Dermatologists." Proceedings of the ETRA 2014. Ed. Unknown. Safety Harbor, Florida: n.P., 2014. Web.
Womack, K., et al. "Markers of Confidence and Correctness in Spoken Medical Narratives." Proceedings of the Interspeech 2013. Ed. ISCA. Lyon, France: n.P., 2013. Web.
Womack, Kathryn, et al. "Using Linguistic Analysis to Characterize Conceptual Units of Thought in Spoken Medical Narratives." Proceedings of the Interspeech 2013. Ed. ISCA. Lyon, France: n.P., 2013. Web.
Womack, Kathryn, et al. "Disfluencies as Extra-Propositional Indicators of Cognitive Processing." Proceedings of the Ex-Prom-2012: Workshop on Extra-Propositional Aspect of Meaning in Computational Linguistics at the 50th Annual Meeting of the Association for Computational Linguistics 2012. Ed. Roser Morante and Caroline Sporleder. Jeju, Korea: n.P., 2013. Web.
McCoy, Wilson. "Annotation Schemes to Encode Domain Knowledge in Medical Narratives." Proceedings of the 6th Linguistic Annotation Workshop at the 50th Annual Meeting of the Associate. For Computational Linguistics 2012. Ed. Nancy Ide and Fei Xia. Jeju, Korea: n.P., 2012. Web.
McCoy, Wilson, et al. "Linking Uncertainty in Physicians' Narratives to Diagnostic Correctness." Proceedings of the ExProm-2012: Workshop on Extra-Propositional Aspect of Meaning in Computational Linguistics at the 50th Annual Meeting of the Association for Computational Linguistics. Ed. Roser Morante and Caroline Sporleder. Jeju, Korea: n.P., 2013. Web.
Lehrman, Michael T., Cecilia O. Alm, and Ruben A. Proano. "Detecting Distressed vs. Non- Distressed Affect State in Short Forum Texts." Proceedings of the 2012 Workshop on Language in Social Media at the Conference of the North Am. Chapter of the Associate. For Comp. Linguistics-Human Language Technologies. Ed. ACL/NAACL-HLT (Meena Nagarajan, Sara Owsley Sood, Michael Gamon). Montreal, Canada: n.P., 2012. Web.
Li, Rui, et al. "Learning Eye Movement Patterns for Characterization of Perceptual Expertise." Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA). Ed. ETRA. Santa Barbara, CA: n.P., 2012. Web.
Alm, Cecilia O. "Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications." Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Ed. Dekang Lin, Yuji Matsumoto, Rada Mihalcea. Portland, OR: The Association for Computational Linguistic, 2011. Web.
The Use Of Artificial Intelligence And Natural Language Processing For Mental Health Interventions
In a recent article published in Translational Psychiatry, researchers performed a systemic review and meta-analysis of scientific papers using an artificial intelligence (AI)-based tool that uses Natural Language Processing (NLP) to examine mental health interventions (MHI).
Study: Natural language processing for mental health interventions: a systematic review and research framework. Image Credit: MMD Creative/Shutterstock.Com
BackgroundGlobally, neuropsychiatric disorders, such as depression and anxiety, pose a significant economic burden on healthcare systems. The financial burden of mental health diseases is estimated to reach six trillion US dollars annually by 2030.
Numerous MHIs, including behavioral, psychosocial, pharmacological, and telemedicine, appear effective in promoting the well-being of affected individuals. However, their inherent systemic issues limit their effectiveness and ability to meet increasing demand.
Moreover, the clinical workforce is scarce, needs extensive training for mental health assessments, the quality of available treatment is variable, and current quality assurance practices cannot handle reduced effect sizes of widespread MHIs.
Given the low quality of MHIs, especially in developing countries, there is a need for more research on developing tools, especially ML-based tools, that facilitate mental health diagnosis and treatment.
NLP facilitates the quantitative study of conversation transcripts and medical records for thousands of patients in no time. It renders words into numeric and graphical representations, a task previously considered unfathomable. More importantly, it could examine the characteristics of providers and patients to detect meaningful trends in large datasets.
Digital health platforms have made MHI data more readily available, making it possible for NLP tools to do many analyses related to studying treatment fidelity, patient outcomes, treatment components, therapeutic alliance, and gauging suicide risk.
Lastly, NLP could analyze social media data and electronic health records (EHRs) in mental health-relevant contexts.
While NLP has shown research potential, the current separation between clinical and computer science researchers has limited its impact on clinical practice.
Thus, even though the use of machine learning in the mental health domain has increased, clinicians have not included peer-reviewed manuscripts from AI conferences reporting advances in NLP.
About the studyIn the present study, researchers classified NLP methods deployed to study MHI, identified clinical domains, and used them to aggregate NLP findings.
They examined the main features of the NLP pipeline in each manuscript, including linguistic representations, software packages, classification, and validation methods. Likewise, they evaluated its clinical settings, goals, transcript origin, clinical measures, ground truths, and raters.
Moreover, the researchers evaluated NLP-MHI studies to identify common areas, biases, and knowledge gaps in applying NLP to MHI to propose a research framework that could aid computer and clinical researchers in improving the clinical utility of these tools.
They screened articles on the Pubmed, PsycINFO, and Scopus databases to identify studies focused solely on NLP for human-to-human MHI for assessing mental health, e.G., psychotherapy, patient assessment, psychiatric treatment, crisis counseling, to name a few.
Further, the researchers searched peer-reviewed AI conferences (e.G., Association for Computational Linguistics) through ArXiv and Google Scholar.
They compiled articles that met five criteria:
i) were original empirical studies;
ii) published in English;
iii)peer-reviewed;
iv) MHI-focused; and
v) analyzed MHI-retrieved textual data (e.G., transcripts).
ResultsThe final sample set comprised 102 studies, primarily involving face-to-face randomized controlled trials (RCTs), conventional treatments, and collected therapy corpora.
Nearly 54% of these studies were published between 2020 and 2022, suggesting a surge in NLP-based methods for MHI applications.
Six clinical categories emerged in the review: two and two for the patients and providers, respectively, and two for patient-provider interactions.
These were clinical presentation, intervention response (for patients), intervention monitoring, provider characteristics (for clinicians), relational dynamics, and conversational topics (interaction). They all operated simultaneously as factors in all treatment outcomes.
While clinicians provided ground truth ratings for 31 studies, patients did so through self-report measures of symptom feedback and treatment alliance ratings for 22 studies. The most prevalent source of provider/patient information was Motivational Interviewing Skills Codes (MISC) annotations.
Multiple NLP approaches emerged, reflecting the temporal development of NLP tools. It shows growth and transformations in patient-provider conversations concerning linguistic representations. Word Embeddings were used the most for language representation, i.E., in 48% of studies.
The two most prevalent NLP model features were lexicons and sentiment analysis, as reflected by their use in 43 and 32 studies. The latter generated feature scores for emotions (e.G., joy) derived from lexicon-based methods.
Eventually, context-sensitive deep neural networks replaced word count and frequency-based lexicon methods in NLP models. A total of 16 studies also used topic modeling to identify common themes across clinical transcripts.
After linguistic content, acoustic characteristics emerged as a promising source of treatment data, with 16 studies examining the same from the speech of patients and providers.
The authors noted that research in this area showed immense progress in mental health diagnoses and treatment specifications. It also remarkably identified the quality of therapeutics for the patient.
Accordingly, they proposed integrating these distinctive contributions into one framework (NLPxMHI) that helped computational and clinical researchers collaborate and outlined novel NLP applications for innovations in mental health services.
Only 40 studies reported demographic information for the dataset used. So, the authors recommended that NLPxMHI researchers document the demographic data of all individuals participating in their models' training and evaluation.
In addition, they emphasized the over-sampling of underrepresented groups to help address biases and improve the representativeness of NLP models.
Further, they recommended representing treatment as sequential actions to improve the accuracy of intervention studies, emphasizing the importance of timing and context in enriching beneficial effects. Integrating identified clinical categories into a unified model could also help investigators increase the richness of treatment recommendations.
Fewer reviewed studies implemented techniques to enhance interpretability. It likely hindered investigators from interpreting the overall behavior of the NLP models (across inputs).
Nonetheless, ongoing collaboration between clinical and computational domains will slowly fill the gap between interpretability and accuracy through clinical review, model tuning, and generalizability. In the future, it might help outline valid treatment decision rules and fulfill the promise of precision medicine.
ConclusionsOverall, NLP methods have the potential to operationalize MHI. Its proof-of-concept applications have shown promise in addressing systemic challenges.
However, as the NLPxMHI framework bridges research designs and disciplines, it would also require the support of large secure datasets, a common language, and equity checks for continued progress.
The authors anticipate that this could revolutionize the assessments and treatments of mental health diseases.
Introduction To Computational Linguistics And Natural-language Processing - Fall 2023
Natural-language-processing applications are ubiquitous: Alexa can set a reminder, or play a particular song, or provide your local weather if you ask; Google Translate can make documents readable across languages; ChatGPT can be prompted to generate convincingly fluent text, which is often even correct. How do such systems work? This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as these. You will work with ideas from linguistics, statistical modeling, machine learning, and neural networks, with emphasis on their application, limitations, and implications. The course is lab- and project-based, primarily in small teams, and culminates in the building and testing of a question-answering system.
For more information about this course, visit the Harvard University Course Catalog.
