Identifying associations between epidemiological entities in news data for animal disease surveillance

Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelli...
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Gespeichert in:

Autor*in:	Sarah Valentin [verfasserIn] Renaud Lancelot [verfasserIn] Mathieu Roche [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Animal disease surveillance Text mining Event extraction

Übergeordnetes Werk:	In: Artificial Intelligence in Agriculture - KeAi Communications Co., Ltd., 2020, 5(2021), Seite 163-174
Übergeordnetes Werk:	volume:5 ; year:2021 ; pages:163-174

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.aiia.2021.07.003

Katalog-ID:	DOAJ086749773

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allfieldsSound	10.1016/j.aiia.2021.07.003 doi (DE-627)DOAJ086749773 (DE-599)DOAJ8586da22bcb8462ab198a578923cc886 DE-627 ger DE-627 rakwb eng Sarah Valentin verfasserin aut Identifying associations between epidemiological entities in news data for animal disease surveillance 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelligence, and expertise. In this context, our work pertins to the extraction of epidemiological events in textual data (i.e. news) by unsupervised methods. We define the event extraction task as detecting pairs of epidemiological entities (e.g. a disease name and location). The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics. We used a publicly available annotated corpus of 438 documents (i.e. news articles) related to animal disease events. The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall. Our results showed that using a window of words outperformed document-based and sentence-based approaches, while reducing the probability of detecting false pairs. Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework. We believe that Mutual Information would be more relevant for rare pair detection (i.e. weak signals), but requires higher manual curation to avoid false positive extraction pairs. Moreover, generalising the country-level spatial features enabled better discrimination (i.e. ranking) of relevant disease-location pairs for event extraction. Animal disease surveillance Text mining Event extraction Agriculture S Renaud Lancelot verfasserin aut Mathieu Roche verfasserin aut In Artificial Intelligence in Agriculture KeAi Communications Co., Ltd., 2020 5(2021), Seite 163-174 (DE-627)1663653283 (DE-600)2970491-1 25897217 nnns volume:5 year:2021 pages:163-174 https://doi.org/10.1016/j.aiia.2021.07.003 kostenfrei https://doaj.org/article/8586da22bcb8462ab198a578923cc886 kostenfrei http://www.sciencedirect.com/science/article/pii/S2589721721000246 kostenfrei https://doaj.org/toc/2589-7217 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 163-174
language	English
source	In Artificial Intelligence in Agriculture 5(2021), Seite 163-174 volume:5 year:2021 pages:163-174
sourceStr	In Artificial Intelligence in Agriculture 5(2021), Seite 163-174 volume:5 year:2021 pages:163-174
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Animal disease surveillance Text mining Event extraction Agriculture S
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container_title	Artificial Intelligence in Agriculture
authorswithroles_txt_mv	Sarah Valentin @@aut@@ Renaud Lancelot @@aut@@ Mathieu Roche @@aut@@
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author	Sarah Valentin
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topic_title	Identifying associations between epidemiological entities in news data for animal disease surveillance Animal disease surveillance Text mining Event extraction
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title	Identifying associations between epidemiological entities in news data for animal disease surveillance
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title_full	Identifying associations between epidemiological entities in news data for animal disease surveillance
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title_sort	identifying associations between epidemiological entities in news data for animal disease surveillance
title_auth	Identifying associations between epidemiological entities in news data for animal disease surveillance
abstract	Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelligence, and expertise. In this context, our work pertins to the extraction of epidemiological events in textual data (i.e. news) by unsupervised methods. We define the event extraction task as detecting pairs of epidemiological entities (e.g. a disease name and location). The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics. We used a publicly available annotated corpus of 438 documents (i.e. news articles) related to animal disease events. The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall. Our results showed that using a window of words outperformed document-based and sentence-based approaches, while reducing the probability of detecting false pairs. Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework. We believe that Mutual Information would be more relevant for rare pair detection (i.e. weak signals), but requires higher manual curation to avoid false positive extraction pairs. Moreover, generalising the country-level spatial features enabled better discrimination (i.e. ranking) of relevant disease-location pairs for event extraction.
abstractGer	Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelligence, and expertise. In this context, our work pertins to the extraction of epidemiological events in textual data (i.e. news) by unsupervised methods. We define the event extraction task as detecting pairs of epidemiological entities (e.g. a disease name and location). The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics. We used a publicly available annotated corpus of 438 documents (i.e. news articles) related to animal disease events. The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall. Our results showed that using a window of words outperformed document-based and sentence-based approaches, while reducing the probability of detecting false pairs. Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework. We believe that Mutual Information would be more relevant for rare pair detection (i.e. weak signals), but requires higher manual curation to avoid false positive extraction pairs. Moreover, generalising the country-level spatial features enabled better discrimination (i.e. ranking) of relevant disease-location pairs for event extraction.
abstract_unstemmed	Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelligence, and expertise. In this context, our work pertins to the extraction of epidemiological events in textual data (i.e. news) by unsupervised methods. We define the event extraction task as detecting pairs of epidemiological entities (e.g. a disease name and location). The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics. We used a publicly available annotated corpus of 438 documents (i.e. news articles) related to animal disease events. The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall. Our results showed that using a window of words outperformed document-based and sentence-based approaches, while reducing the probability of detecting false pairs. Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework. We believe that Mutual Information would be more relevant for rare pair detection (i.e. weak signals), but requires higher manual curation to avoid false positive extraction pairs. Moreover, generalising the country-level spatial features enabled better discrimination (i.e. ranking) of relevant disease-location pairs for event extraction.
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title_short	Identifying associations between epidemiological entities in news data for animal disease surveillance
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Identifying associations between epidemiological entities in news data for animal disease surveillance

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