Kathleen C. Fraser

Also published as: Kathleen Fraser


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Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
Isar Nejadgholi | Kathleen C. Fraser | Berry de Bruijn
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch. Here we focus on span mismatch and show that its severity can vary from a serious error to a fully acceptable entity extraction due to the subjectivity of span annotations. For a domain-specific BERT-based NER system, we showed that 25% of the errors have the same labels and overlapping span with gold standard entities. We collected expert judgement which shows more than 90% of these mismatches are accepted or partially accepted by the user. Using the training set of the NER system, we built a fast and lightweight entity classifier to approximate the user experience of such mismatches through accepting or rejecting them. The decisions made by this classifier are used to calculate a learning-based F-score which is shown to be a better approximation of a forgiving user’s experience than the relaxed F-score. We demonstrated the results of applying the proposed evaluation metric for a variety of deep learning medical entity recognition models trained with two datasets.


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Recognizing UMLS Semantic Types with Deep Learning
Isar Nejadgholi | Kathleen C. Fraser | Berry De Bruijn | Muqun Li | Astha LaPlante | Khaldoun Zine El Abidine
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.

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How do we feel when a robot dies? Emotions expressed on Twitter before and after hitchBOT’s destruction
Kathleen C. Fraser | Frauke Zeller | David Harris Smith | Saif Mohammad | Frank Rudzicz
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada. It similarly made its way across Germany and the Netherlands, and had begun a trip across the USA when it was destroyed by vandals. In this work, we analyze the emotions and sentiments associated with words in tweets posted before and after hitchBOT’s destruction to answer two questions: Were there any differences in the emotions expressed across the different countries visited by hitchBOT? And how did the public react to the demise of hitchBOT? Our analyses indicate that while there were few cross-cultural differences in sentiment towards hitchBOT, there was a significant negative emotional reaction to its destruction, suggesting that people had formed an emotional connection with hitchBOT and perceived its destruction as morally wrong. We discuss potential implications of anthropomorphism and emotional attachment to robots from the perspective of robot ethics.

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The importance of sharing patient-generated clinical speech and language data
Kathleen C. Fraser | Nicklas Linz | Hali Lindsay | Alexandra König
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

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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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling
Kathleen C. Fraser | Nicklas Linz | Bai Li | Kristina Lundholm Fors | Frank Rudzicz | Alexandra König | Jan Alexandersson | Philippe Robert | Dimitrios Kokkinakis
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.


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A Swedish Cookie-Theft Corpus
Dimitrios Kokkinakis | Kristina Lundholm Fors | Kathleen Fraser | Arto Nordlund
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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An analysis of eye-movements during reading for the detection of mild cognitive impairment
Kathleen C. Fraser | Kristina Lundholm Fors | Dimitrios Kokkinakis | Arto Nordlund
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy.


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Detecting late-life depression in Alzheimer’s disease through analysis of speech and language
Kathleen C. Fraser | Frank Rudzicz | Graeme Hirst
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology


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Using linguistic features longitudinally to predict clinical scores for Alzheimer’s disease and related dementias
Maria Yancheva | Kathleen Fraser | Frank Rudzicz
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Sentence segmentation of aphasic speech
Kathleen C. Fraser | Naama Ben-David | Graeme Hirst | Naida Graham | Elizabeth Rochon
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Comparison of different feature sets for identification of variants in progressive aphasia
Kathleen C. Fraser | Graeme Hirst | Naida L. Graham | Jed A. Meltzer | Sandra E. Black | Elizabeth Rochon
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Using statistical parsing to detect agrammatic aphasia
Kathleen C. Fraser | Graeme Hirst | Jed A. Meltzer | Jennifer E. Mack | Cynthia K. Thompson
Proceedings of BioNLP 2014


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Automatic speech recognition in the diagnosis of primary progressive aphasia
Kathleen Fraser | Frank Rudzicz | Naida Graham | Elizabeth Rochon
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies