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  1. Article ; Online: An exploratory study on dialect density estimation for children and adult's African American Englisha).

    Johnson, Alexander / Shankar, Natarajan Balaji / Ostendorf, Mari / Alwan, Abeer

    The Journal of the Acoustical Society of America

    2024  Volume 155, Issue 4, Page(s) 2836–2848

    Abstract: This paper evaluates an innovative framework for spoken dialect density prediction on children's and adults' African American English. A speaker's dialect density is defined as the frequency with which dialect-specific language characteristics occur in ... ...

    Abstract This paper evaluates an innovative framework for spoken dialect density prediction on children's and adults' African American English. A speaker's dialect density is defined as the frequency with which dialect-specific language characteristics occur in their speech. Rather than treating the presence or absence of a target dialect in a user's speech as a binary decision, instead, a classifier is trained to predict the level of dialect density to provide a higher degree of specificity in downstream tasks. For this, self-supervised learning representations from HuBERT, handcrafted grammar-based features extracted from ASR transcripts, prosodic features, and other feature sets are experimented with as the input to an XGBoost classifier. Then, the classifier is trained to assign dialect density labels to short recorded utterances. High dialect density level classification accuracy is achieved for child and adult speech and demonstrated robust performance across age and regional varieties of dialect. Additionally, this work is used as a basis for analyzing which acoustic and grammatical cues affect machine perception of dialect.
    MeSH term(s) Humans ; Black or African American ; Adult ; Child ; Speech Acoustics ; Male ; Female ; Speech Production Measurement/methods ; Language ; Child, Preschool ; Young Adult ; Speech Perception ; Adolescent ; Phonetics ; Child Language
    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 219231-7
    ISSN 1520-8524 ; 0001-4966
    ISSN (online) 1520-8524
    ISSN 0001-4966
    DOI 10.1121/10.0025771
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes.

    Zhou, Sitong / Lybarger, Kevin / Yetisgen, Meliha / Ostendorf, Mari

    AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

    2023  Volume 2023, Page(s) 622–631

    Abstract: Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different ... ...

    Abstract Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction method. To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain. Additionally, we pretrain the transformer language model (LM) on task-related unlabeled texts for better representation. Our experiments indicate that masking and adaptive pretraining methods can significantly improve performance when the source domain is more distant from the target domain.
    Language English
    Publishing date 2023-06-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2676378-3
    ISSN 2153-4063 ; 2153-4063
    ISSN (online) 2153-4063
    ISSN 2153-4063
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: OrchestraLLM

    Lee, Chia-Hsuan / Cheng, Hao / Ostendorf, Mari

    Efficient Orchestration of Language Models for Dialogue State Tracking

    2023  

    Abstract: Large language models (LLMs) have revolutionized the landscape of Natural Language Processing systems, but are computationally expensive. To reduce the cost without sacrificing performance, previous studies have explored various approaches to harness the ...

    Abstract Large language models (LLMs) have revolutionized the landscape of Natural Language Processing systems, but are computationally expensive. To reduce the cost without sacrificing performance, previous studies have explored various approaches to harness the potential of Small Language Models (SLMs) as cost-effective alternatives to their larger counterparts. Driven by findings that SLMs and LLMs exhibit complementary strengths in a structured knowledge extraction task, this work presents a novel SLM/LLM routing framework designed to improve computational efficiency and enhance task performance. First, exemplar pools are created to represent the types of contexts where each LM provides a more reliable answer, leveraging a sentence embedding fine-tuned so that context similarity is close to dialogue state similarity. Then, during inference, the k-nearest exemplars to the testing instance are retrieved, and the instance is routed according to majority vote. In dialogue state tracking tasks, the proposed routing framework enhances performance substantially compared to relying solely on LLMs, while reducing the computational costs by over 50%.
    Keywords Computer Science - Computation and Language
    Subject code 000
    Publishing date 2023-11-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Building blocks for complex tasks

    Zhou, Sitong / Yetisgen, Meliha / Ostendorf, Mari

    Robust generative event extraction for radiology reports under domain shifts

    2023  

    Abstract: This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better ... ...

    Abstract This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ BERT-based task-specific classification layers. We then develop methods that reduce the inference cost of the model, making large-scale corpus processing more feasible for clinical applications. Specifically, we introduce a generative technique that decomposes complex tasks into smaller subtask blocks, which improves a single-pass model when combined with multitask training. In addition, we leverage target-domain contexts during inference to enhance domain adaptation, enabling use of smaller models. Analyses offer insights into the benefits of different cost reduction strategies.
    Keywords Computer Science - Computation and Language
    Subject code 006 ; 004
    Publishing date 2023-06-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts

    Tran, Trang / Ostendorf, Mari

    2021  

    Abstract: This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic ... ...

    Abstract This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic feature extraction with parsing. We assess the utility of the prosody in parsing on imperfect transcripts, i.e. transcripts with automatic speech recognition (ASR) errors, by applying the parser in an N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of the oracle N-best gain relative to parsing the 1-best ASR output, with insignificant impact on word recognition error rate. Prosody provides a significant part of the gain, and analyses suggest that it leads to more grammatical utterances via recovering function words.

    Comment: Interspeech 2021
    Keywords Computer Science - Computation and Language
    Publishing date 2021-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework.

    Lybarger, Kevin / Ostendorf, Mari / Thompson, Matthew / Yetisgen, Meliha

    ArXiv

    2021  

    Abstract: Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of ... ...

    Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information. The automatically extracted symptoms improve prediction performance, beyond structured data alone.
    Language English
    Publishing date 2021-03-10
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework.

    Lybarger, Kevin / Ostendorf, Mari / Thompson, Matthew / Yetisgen, Meliha

    Journal of biomedical informatics

    2021  Volume 117, Page(s) 103761

    Abstract: Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of ... ...

    Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.
    MeSH term(s) COVID-19/diagnosis ; Electronic Health Records ; Humans ; Information Storage and Retrieval ; Natural Language Processing ; Symptom Assessment
    Language English
    Publishing date 2021-03-26
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2021.103761
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Editorial message from the vice president of publications on new developments in Signal Processing Society publications.

    Ostendorf, Mari

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2012  Volume 21, Issue 11, Page(s) 4506–4507

    Language English
    Publishing date 2012-07-01
    Publishing country United States
    Document type Editorial
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/tip.2012.2218440
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes

    Zhou, Sitong / Lybarger, Kevin / Yetisgen, Meliha / Ostendorf, Mari

    2022  

    Abstract: Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different ... ...

    Abstract Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction method. To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain. Additionally, we pretrain the transformer language model (LM) on task-related unlabeled texts for better representation. Our experiments indicate that masking and adaptive pretraining methods can significantly improve performance when the source domain is more distant from the target domain.
    Keywords Computer Science - Computation and Language
    Subject code 400 ; 006
    Publishing date 2022-09-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction.

    Lybarger, Kevin / Ostendorf, Mari / Yetisgen, Meliha

    Journal of biomedical informatics

    2020  Volume 113, Page(s) 103631

    Abstract: Social determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text requires data-driven information extraction models trained on annotated ... ...

    Abstract Social determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text requires data-driven information extraction models trained on annotated corpora that are heterogeneous and frequently include critical SDOH. This work presents a new corpus with SDOH annotations, a novel active learning framework, and the first extraction results on the new corpus. The Social History Annotation Corpus (SHAC) includes 4480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events. We introduce a novel active learning framework that selects samples for annotation using a surrogate text classification task as a proxy for a more complex event extraction task. The active learning framework successfully increases the frequency of health risk factors and improves automatic extraction of these events over undirected annotation. An event extraction model trained on SHAC achieves high extraction performance for substance use status (0.82-0.93 F1), employment status (0.81-0.86 F1), and living status type (0.81-0.93 F1) on data from three institutions.
    MeSH term(s) Information Storage and Retrieval ; Natural Language Processing ; Risk Factors ; Social Determinants of Health
    Language English
    Publishing date 2020-12-05
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2020.103631
    Database MEDical Literature Analysis and Retrieval System OnLINE

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