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  • Authors: Key-DeLyria, Sarah E.;

    Purpose: Sentence processing can be affected following a traumatic brain injury (TBI) due to linguistic or cognitive deficits. Language-related event-related potentials (ERPs), particularly the P600, have not been described in individuals with TBI history.Method: Four young adults with a history of closed head injury participated. Two had severe injuries, and 2 had mild���moderate injuries more than 24 months prior to testing. ERPs were recorded while participants read sentences designed to be grammatically correct or incorrect. Participants also completed cognitive and sentence comprehension measures.Results: One participant with TBI was significantly different than the control group on several behavioral sentence measures and 1 cognitive measure. However, none of the participants with TBI had a reliable P600 effect. Nonparametric bootstrapping indicated that the ERP was reliable in 10 control participants but no participants with TBI history.Conclusions: There were few behavioral differences between individuals with TBI history and the control group, though all reported subjective difficulty with reading. The P600 was absent in the TBI group in this study. Given the heterogeneity of individuals with TBI and the difficulty in assessing subtle language impairments, exploring the P600 further may provide useful insight into language processing difficulties.

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  • Authors: Abraham, Maryvonne;

    International audience; This paper presents a discussion about assistive and augmentative natural language processing designed for certain disabled persons unable to communicate. Several approaches have been proposed, according to abilities of the writer. Here we distinguish two cases in the writers capacities: the writer knows alphabetic writing, or (s)he does not know it. In the first case, the idea is to assist the writer by completing the words or the group of words which are initially written. In the second case, pictograms are used instead of characters, but it must be decided if these pictograms represent concepts or words in a new writing system. If the pictograms represent concepts, the produced text may not correspond exactly to the wishes of the writer; whereas when the pictograms represent words, the writer has to change his (her) mental approach to write the words that (s)he has chosen in another way.

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  • Authors: Nazarenko, Adeline; Aubin, Sophie; Bossy, Robert; Erjavec, Tomaz; +6 Authors

    This document describes the ALVIS NLP line that has been developed. In ALVIS, it is used to analyze the crawled documents that are then indexed on semantic and domain-specific grounds. It is also to process training corpora in order to acquire the specialized linguistic and domain resources (in WP6) that are then exploited for the semantic analysis of larger document collections. Four languages are addressed in ALVIS: English, French, Slovene and Chinese. Some specific NLP modules have been developed and integrated for each language. The resulting NLP lines have been tested trough several experiments on different domains, on document collections of various size and with various degrees of NLP analysis.

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    Authors: Zabotkina, Vera Ivanovna; Kotov, Artemy; POZDNYAKOVA, ELENA M;

    The study presents the experimentally developed technique to determine how and where semantic categories are represented in the human brain. For this purpose, the combination of linguistic annotation of oral texts in Russian with the registration of BOLD signal in functional MRI subjects’ testing was applied. The goal is to correlate groups of words in input texts and patterns of brain activation. The algorithm of the texts’ markup consisted of four sequential procedures: (1) time annotation, (2) lemmatization, (3) vectorization, (4) indexation of words-attributes. Regarding the issue of brain activity allocation, the corresponding activation distributed nature can be noted. It proves our hypothesis that conceptual information during text comprehension is processed in different (however specific) areas of the brain and is not compactly localized. The main result is that the allocated lexical clusters are composed of paradigmatically close words (synonyms, hyponyms) and situationally related words.

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    Authors: Taejin L. Min; Liyan Xu; Jinho D. Choi; Ranliang Hu; +6 Authors

    Rationale and Objectives We aimed to assess early COVID-19 pandemic-associated changes in brain MRI examination frequency and acuity of imaging findings acuity. Methods Using a natural language processing model, we retrospectively categorized reported findings of 12,346 brain MRI examinations performed during 6-month pre-pandemic and early pandemic time periods across a large metropolitan health system into three acuity levels: 1, normal or near normal; 2, incidental or chronic findings not requiring a management change; and 3, new or progressive findings requiring a management change. Brain MRI frequency and imaging finding acuity level were compared over time. Results Between March and August of 2019 (pre-pandemic) and 2020 (early pandemic), our health system brain MRI examination volumes decreased 17.0% (6,745 vs. 5,601). Comparing calendar-matched 6-month periods, the proportion of higher acuity findings increased significantly (p <0.001) from pre-pandemic (22.5%, 43.6% and 34.0% in acuity level 1, 2, and 3, respectively) to early pandemic periods (19.1%, 40.9%, and 40.1%). During the second three months of the early pandemic period, as MRI volumes recovered to near baseline, the proportion of higher acuity findings remained high (42.6% vs. 34.1%) compared with a similar pre-pandemic period. In a multivariable analysis, Black (B coefficient, 0.16) and underinsured population (B coefficient, 0.33) presented with higher acuity findings (p < 0.05). Conclusion As the volume of brain MRI examinations decreased during the early COVID-19 pandemic, the relative proportion of examinations with higher acuity findings increased significantly. Pandemic-related changes in patient outcomes related to reduced imaging access merits further attention.

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    Current Problems in Diagnostic Radiology
    Article . 2022 . Peer-reviewed
    License: Elsevier TDM
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      Current Problems in Diagnostic Radiology
      Article . 2022 . Peer-reviewed
      License: Elsevier TDM
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    Authors: Tina Heger; Sina Zarrieß; Alsayed Algergawy; Jonathan Jeschke; +1 Authors

    Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

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    ZENODO
    Article . 2022
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    Research Ideas and Outcomes
    Article . 2022 . Peer-reviewed
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      Research Ideas and Outcomes
      Article . 2022 . Peer-reviewed
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  • Authors: Ashika, Sharma; Jaikishan, Jayakumar; Partha P, Mitra; Sutanu, Chakraborti; +1 Authors

    Understanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine 'Brain Region Connectivity' information from published articles to build a centralized connectivity resource helping neuroscience researchers to gain quick access to research findings. Manually curating and continuously updating such a resource involves significant time and effort. This paper presents an application of supervised machine learning algorithms that perform shallow and deep linguistic analysis of text to automatically extract connectivity between brain region mentions. Our proposed algorithms are evaluated using benchmark datasets collated from PubMed and our own dataset of full text articles annotated by a domain expert. We also present a comparison with state-of-the-art methods including BioBERT. Proposed methods achieve best recall and [Formula: see text] scores negating the need for any domain-specific predefined linguistic patterns. Our paper presents a novel effort towards automatically generating interpretable patterns of connectivity for extracting connected brain region mentions from text and can be expanded to include any other domain-specific information.

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    Authors: Richardet, Renaud Luc;

    In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles in peer-reviewed journals. One challenge for modern neuroinformatics is to design methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and its integration into computational models. In this thesis, we introduce novel natural language processing (NLP) models and systems to mine the neuroscientific literature. In addition to in vivo, in vitro or in silico experiments, we coin the NLP methods developed in this thesis as in litero experiments, aiming at analyzing and making accessible the extended body of neuroscientific literature. In particular, we focus on two important neuroscientific entities: brain regions and neural cells. An integrated NLP model is designed to automatically extract brain region connectivity statements from very large corpora. This system is applied to a large corpus of 25M PubMed abstracts and 600K full-text articles. Central to this system is the creation of a searchable database of brain region connectivity statements, allowing neuroscientists to gain an overview of all brain regions connected to a given region of interest. More importantly, the database enables researcher to provide feedback on connectivity results and links back to the original article sentence to provide the relevant context. The database is evaluated by neuroanatomists on real connectomics tasks (targets of Nucleus Accumbens) and results in significant effort reduction in comparison to previous manual methods (from 1 week to 2h). Subsequently, we introduce neuroNER to identify, normalize and compare instances of identify neuronsneurons in the scientific literature. Our method relies on identifying and analyzing each of the domain features used to annotate a specific neuron mention, like the morphological term 'basket' or brain region 'hippocampus'. We apply our method to the same corpus of 25M PubMed abstracts and 600K full-text articles and find over 500K unique neuron type mentions. To demonstrate the utility of our approach, we also apply our method towards cross-comparing the NeuroLex and Human Brain Project (HBP) cell type ontologies. By decoupling a neuron mention's identity into its specific compositional features, our method can successfully identify specific neuron types even if they are not explicitly listed within a predefined neuron type lexicon, thus greatly facilitating cross-laboratory studies. In order to build such large databases, several tools and infrastructureslarge-scale NLP were developed: a robust pipeline to preprocess full-text PDF articles, as well as bluima, an NLP processing pipeline specialized on neuroscience to perform text-mining at PubMed scale. During the development of those two NLP systems, we acknowledged the need for novel NLP approaches to rapidly develop custom text mining solutions. This led to the formalization of the agile text miningagile text-mining methodology to improve the communication and collaboration between subject matter experts and text miners. Agile text mining is characterized by short development cycles, frequent tasks redefinition and continuous performance monitoring through integration tests. To support our approach, we developed Sherlok, an NLP framework designed for the development of agile text mining applications.

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    https://doi.org/10.5075/epfl-t...
    Other literature type . 2016
    Data sources: Datacite
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      https://doi.org/10.5075/epfl-t...
      Other literature type . 2016
      Data sources: Datacite
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    Authors: Berkay, Dilara; Jenkins, Adrianna C.;

    Recent advances in Natural Language Processing (NLP) make it possible to quantify relationships among different words extracted from large-scale human text corpora. Using a word embeddings model, we quantified the semantic distance between pairs of adjectives that could describe people or objects (e.g., smart, friendly; round, wooden) and scanned participants using fMRI while they had the opportunity to generalize from one known attribute to an unknown attribute across parametrically varying degrees of semantic distance (e.g., given that this person is smart, how likely are they to be friendly?; given that this furniture is round, how likely is it to be wooden?). Across categories, we observed a positive parametric effect of semantic distance on activation in the dorsomedial prefrontal cortex (DMPFC). Results connect to this region’s role in abstraction and inference under reducible uncertainty, with implications for understanding how people generalize beyond what they know to make inferences about novel individuals, items, or experiences.

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    Our dataset is focused on automatic voice recognition for the purpose of diagnosing language disorders; hence, our dataset consists of written documents. As a whole, our study dataset comprises of inquiries, assertive speech, and responses to all three. Young children are being asked these questions and taught these stories. Our focus is on children aged 0 to 6 years. We have polled numerous children within this age range with the permission of their families, including infants from our own family, extended family, neighbors, non-governmental organizations (NGOs) in Bangladesh who work with children, hospitals, and many more. The surveys focused mostly on eliciting responses from the children, whether via direct questioning or age-appropriate aggressive language. Any action or expression of approval in response to the question or statement counts as an answer. Children of varying ages reach several stages in their development of language. A baby who is 7-12 months old may utilize babbling consonant-vowel combinations and consonant sounds; a baby who is 12-20 months old may use gestures, identify their own name, etc. Prior to the experiment, we required to do an analysis of the dataset. Age, Speech in Bangla, Speech Translated in English, Response in Bangla, Response Translated in English, and label are the 6 columns that make up our dataset. We gathered information from kids as young as one month old and as old as sixty (60) months old. In addition, we can see that the number of normal samples is about twice as large as the number of impaired samples (out of 252 total samples. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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  • Authors: Key-DeLyria, Sarah E.;

    Purpose: Sentence processing can be affected following a traumatic brain injury (TBI) due to linguistic or cognitive deficits. Language-related event-related potentials (ERPs), particularly the P600, have not been described in individuals with TBI history.Method: Four young adults with a history of closed head injury participated. Two had severe injuries, and 2 had mild���moderate injuries more than 24 months prior to testing. ERPs were recorded while participants read sentences designed to be grammatically correct or incorrect. Participants also completed cognitive and sentence comprehension measures.Results: One participant with TBI was significantly different than the control group on several behavioral sentence measures and 1 cognitive measure. However, none of the participants with TBI had a reliable P600 effect. Nonparametric bootstrapping indicated that the ERP was reliable in 10 control participants but no participants with TBI history.Conclusions: There were few behavioral differences between individuals with TBI history and the control group, though all reported subjective difficulty with reading. The P600 was absent in the TBI group in this study. Given the heterogeneity of individuals with TBI and the difficulty in assessing subtle language impairments, exploring the P600 further may provide useful insight into language processing difficulties.

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  • Authors: Abraham, Maryvonne;

    International audience; This paper presents a discussion about assistive and augmentative natural language processing designed for certain disabled persons unable to communicate. Several approaches have been proposed, according to abilities of the writer. Here we distinguish two cases in the writers capacities: the writer knows alphabetic writing, or (s)he does not know it. In the first case, the idea is to assist the writer by completing the words or the group of words which are initially written. In the second case, pictograms are used instead of characters, but it must be decided if these pictograms represent concepts or words in a new writing system. If the pictograms represent concepts, the produced text may not correspond exactly to the wishes of the writer; whereas when the pictograms represent words, the writer has to change his (her) mental approach to write the words that (s)he has chosen in another way.

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  • Authors: Nazarenko, Adeline; Aubin, Sophie; Bossy, Robert; Erjavec, Tomaz; +6 Authors

    This document describes the ALVIS NLP line that has been developed. In ALVIS, it is used to analyze the crawled documents that are then indexed on semantic and domain-specific grounds. It is also to process training corpora in order to acquire the specialized linguistic and domain resources (in WP6) that are then exploited for the semantic analysis of larger document collections. Four languages are addressed in ALVIS: English, French, Slovene and Chinese. Some specific NLP modules have been developed and integrated for each language. The resulting NLP lines have been tested trough several experiments on different domains, on document collections of various size and with various degrees of NLP analysis.

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    Authors: Zabotkina, Vera Ivanovna; Kotov, Artemy; POZDNYAKOVA, ELENA M;

    The study presents the experimentally developed technique to determine how and where semantic categories are represented in the human brain. For this purpose, the combination of linguistic annotation of oral texts in Russian with the registration of BOLD signal in functional MRI subjects’ testing was applied. The goal is to correlate groups of words in input texts and patterns of brain activation. The algorithm of the texts’ markup consisted of four sequential procedures: (1) time annotation, (2) lemmatization, (3) vectorization, (4) indexation of words-attributes. Regarding the issue of brain activity allocation, the corresponding activation distributed nature can be noted. It proves our hypothesis that conceptual information during text comprehension is processed in different (however specific) areas of the brain and is not compactly localized. The main result is that the allocated lexical clusters are composed of paradigmatically close words (synonyms, hyponyms) and situationally related words.

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    Authors: Taejin L. Min; Liyan Xu; Jinho D. Choi; Ranliang Hu; +6 Authors

    Rationale and Objectives We aimed to assess early COVID-19 pandemic-associated changes in brain MRI examination frequency and acuity of imaging findings acuity. Methods Using a natural language processing model, we retrospectively categorized reported findings of 12,346 brain MRI examinations performed during 6-month pre-pandemic and early pandemic time periods across a large metropolitan health system into three acuity levels: 1, normal or near normal; 2, incidental or chronic findings not requiring a management change; and 3, new or progressive findings requiring a management change. Brain MRI frequency and imaging finding acuity level were compared over time. Results Between March and August of 2019 (pre-pandemic) and 2020 (early pandemic), our health system brain MRI examination volumes decreased 17.0% (6,745 vs. 5,601). Comparing calendar-matched 6-month periods, the proportion of higher acuity findings increased significantly (p <0.001) from pre-pandemic (22.5%, 43.6% and 34.0% in acuity level 1, 2, and 3, respectively) to early pandemic periods (19.1%, 40.9%, and 40.1%). During the second three months of the early pandemic period, as MRI volumes recovered to near baseline, the proportion of higher acuity findings remained high (42.6% vs. 34.1%) compared with a similar pre-pandemic period. In a multivariable analysis, Black (B coefficient, 0.16) and underinsured population (B coefficient, 0.33) presented with higher acuity findings (p < 0.05). Conclusion As the volume of brain MRI examinations decreased during the early COVID-19 pandemic, the relative proportion of examinations with higher acuity findings increased significantly. Pandemic-related changes in patient outcomes related to reduced imaging access merits further attention.

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    Current Problems in Diagnostic Radiology
    Article . 2022 . Peer-reviewed
    License: Elsevier TDM
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      Current Problems in Diagnostic Radiology
      Article . 2022 . Peer-reviewed
      License: Elsevier TDM
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    Authors: Tina Heger; Sina Zarrieß; Alsayed Algergawy; Jonathan Jeschke; +1 Authors

    Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

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    ZENODO
    Article . 2022
    License: CC BY
    Data sources: ZENODO
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    Research Ideas and Outcomes
    Article . 2022 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Research Ideas and Outcomes
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