Text mining tools for extracting information about microbial biodiversity in food

Chaix, Estelle; Deléger, Louise; Bossy, Robert; Nédellec, Claire

doi:10.1016/j.fm.2018.04.011

Cited by 46 publications

(40 citation statements)

References 44 publications

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“…Text mining can be defined as a process that aims to extract interesting and non-trivial patterns or knowledge from unstructured textual data in document collections (Ananiadou and McNaught 2005, Feldman and Sanger 2007). Text mining has successfully been applied to the biomedical literature (Arighi et al 2013, Wei et al 2013, Mihăilă et al 2015, Ananiadou and Thompson 2017) and more recently, it has also been employed in the biodiversity domain to unlock knowledge hidden in the literature (Ulate 2014, Barrios et al 2015, Batista-Navarro et al 2016, Batista-Navarro et al 2017, Parr and Thessen 2018, Chaix et al 2018).…”

Section: Introductionmentioning

confidence: 99%

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

Nguyen¹,

Gabud²,

Ananiadou³

2019

BDJ

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Background Species occurrence records are very important in the biodiversity domain. While several available corpora contain only annotations of species names or habitats and geographical locations, there is no consolidated corpus that covers all types of entities necessary for extracting species occurrence from biodiversity literature. In order to alleviate this issue, we have constructed the COPIOUS corpus—a gold standard corpus that covers a wide range of biodiversity entities. Results Two annotators manually annotated the corpus with five categories of entities, i.e. taxon names, geographical locations, habitats, temporal expressions and person names. The overall inter-annotator agreement on 200 doubly-annotated documents is approximately 81.86% F-score. Amongst the five categories, the agreement on habitat entities was the lowest, indicating that this type of entity is complex. The COPIOUS corpus consists of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities. Named entity recognisers trained on the corpus could achieve an F-score of 74.58%. Moreover, in recognising taxon names, our model performed better than two available tools in the biodiversity domain, namely the SPECIES tagger and the Global Name Recognition and Discovery. More than 1,600 binary relations of Taxon-Habitat, Taxon-Person, Taxon-Geographical locations and Taxon-Temporal expressions were identified by applying a pattern-based relation extraction system to the gold standard. Based on the extracted relations, we can produce a knowledge repository of species occurrences. Conclusion The paper describes in detail the construction of a gold standard named entity corpus for the biodiversity domain. An investigation of the performance of named entity recognition (NER) tools trained on the gold standard revealed that the corpus is sufficiently reliable and sizeable for both training and evaluation purposes. The corpus can be further used for relation extraction to locate species occurrences in literature—a useful task for monitoring species distribution and preserving the biodiversity.

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Section: Introductionmentioning

confidence: 99%

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

Nguyen¹,

Gabud²,

Ananiadou³

2019

BDJ

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“…Rakhi, Tuwani, Mukherjee, and Bagler (2018) recognized the health benefits of culinary herbs and spices through literature mining. Chaix, Deléger, Bossy, and Nédellec (2019) applied text mining to a big collection of PubMed scientific paper abstracts to identify ecological diversity and the origin of microbial presence in food.…”

Section: Food Knowledge Discoverymentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

154

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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“…Moreover, it requires specific skills to implement these algorithms. For instance, text mining tools have been implemented to extract information about microbial biodiversity in food 10 . However, this search cannot be extended to another subject without re-calibration.…”

Section: /24mentioning

confidence: 99%

“…These methods are very effective when the research question is clearly formulated, however, they afford little guidance when the informational need of the researcher is less circumscribed, for instance looking for risk factors or predictive factors. There are also many methods using machine learning algorithms [7][8][9] , or methods focused on specific topics 10 .…”

Section: Introductionmentioning

confidence: 99%

Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

Bonnet

Jean-Pierre

Landais

2020

Preprint

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Background: Literature search is challenging when thousands of articles are potentially involved. To facilitate literature search we created TEMAS a Text Mining Algorithm-assisted Search tool that we compared to a PubMed reference search (RS) in the context of etiological epidemiology.Methods: The 4 steps of TEMAS are: 1) a classic PubMed global search 2) a first sort removing articles without abstracts or containing off-topic terms 3) a clustering step with a descending hierarchical classification regrouping articles in independent classes 4) a final sort extracting from the targeted class the abstracts containing the terms of interest, with a link to the corresponding PubMed articles. Validation was performed for risk factors of breast cancer. We estimated the precision and recall rate compared to RS. Average precision and discounted cumulative gain (DCG) were also computed to perform a ranking-based evaluation. We also compared TEMAS results with articles selected in two meta-analyses.Results: For risk factors of breast cancer, breastfeeding, mammographic density, oral contraceptive, and menarche were explored. TEMAS consistently increased precision vs RS (from 23% to 32%), with a recall rate from 95% to 97%, and divided the number of selected articles to read from 2.3 to 4.8 times. Mean average precision for 100 articles was 47.4% for TEMAS vs 20.9% for PubMed ranked by best match, and DCG showed a consistent improvement for TEMAS compared to PubMed best match.Discussion: TEMAS divided the results of a literature search by 3.2, and improved the precision rate, the average precision, and the DCG compared to RS for epidemiological studies. Reducing the number of selected articles inevitably impacted the recall rate. However, it remained satisfactory and did not bias the corpus of information. Moreover, the recall rate was 100% for the two meta-analyses we analyzed, which suggests that the loss of recall rate observed above concerned articles not relevant enough to be included in the meta-analyses.Conclusion: TEMAS provides a user-friendly interface for non-specialists of literature search confronted with thousands of articles and appeared useful for meta-analyses.

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Text mining tools for extracting information about microbial biodiversity in food

Cited by 46 publications

References 44 publications

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

Utilization of text mining as a big data analysis tool for food science and nutrition

Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

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