Detection and categorization of bacteria habitats using shallow linguistic analysis

Karadeniz, İlknur; Özgür, Arzucan

doi:10.1186/1471-2105-16-s10-s5

Cited by 8 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in the COPIOUS project, to support the conservation of the Philippine biodiversity, our initial emphasis is on organisms that are highly endangered with extinction, such as birds, fish, mammals and plants; microorganisms will be dealt with in future work. As a result, the types of taxon names and habitats annotated in Bacteria Biotope and recognised by tools trained on the corpus (Björne and Salakoski 2015, Karadeniz and Özgür 2015, Lavergne et al 2015) are not suitable for supporting our immediate aims. While Bacteria Biotope concerns the biomedical domain, the other corpora mentioned above, i.e.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

Gabud²,

Ananiadou³

2019

BDJ

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

Gabud²,

Ananiadou³

2019

BDJ

View full text Add to dashboard Cite

show abstract

“…Features were also extended between mentions and antecedents. Recognizing the importance of coreference features, several systems [181][182][183][184] subsequently implemented coreference resolution across sentences. They showed that to facilitate extracting specific types of relations, heuristic/rule-based coreference resolution tends to outperform domain-adapted statistical machine learning systems.…”

Section: Integrating Coreference Resolutionmentioning

confidence: 99%

Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations

2016

View full text Add to dashboard Cite

Research on extracting biomedical relations has received growing attention recently, with numerous biological and clinical applications including those in pharmacogenomics, clinical trial screening and adverse drug reaction detection. The ability to accurately capture both semantic and syntactic structures in text expressing these relations becomes increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research. Significant progress has been made in algorithm development and resource construction. In particular, graph-based approaches bridge semantics and syntax, often achieving the best performance in shared tasks. However, a number of problems at the frontiers of biomedical relation extraction continue to pose interesting challenges and present opportunities for great improvement and fruitful research. In this article, we place biomedical relation extraction against the backdrop of its versatile applications, present a gentle introduction to its general pipeline and shared resources, review the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions.

show abstract

“…Bannour et al (2013) combined automatically learned rules from training set via machine learning and the rules constructed on the basis of the OntoBiotope ontology to identify bacteria and habitat entities (Nédellec, 2015). Karadeniz and Özgür (2015) designed recognition rules based on multiple shallow syntactic analysis such as sentence segmentation, part-of-speech tagging, partial parsing, and lemmatization. Rule-based methods relies heavily on domain thesauri.…”

Section: Bacteria Biotope Ner Taskmentioning

confidence: 99%

Identifying bacterial biotope entities using sequence labeling: Performance and feature analysis

Mao

Cui

2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

Habitat information is important to biodiversity conservation and research. Extracting bacterial biotope entities from scientific publications is important to large scale study of the relationships between bacteria and their living environments. To facilitate the further development of robust habitat text mining systems for biodiversity, following the BioNLP task framework, three sequence labeling techniques, CRFs (Conditional Random Fields), MEMM (Maximum Entropy Markov Model) and SVM hmm (Support Vector Machine) and one classifier, SVM multiclass , are compared on their performance in identifying three types of bacterial biotope entities: bacteria, habitats and geographical locations. The effectiveness of a variety of basic word formation features, syntactic features, and semantic features are exploited and compared for the three sequence labeling methods. Experiments on two publicly available BioNLP collections show that, in addition to a WordNet feature, word embedding featured clusters (although not trained with the task-specific corpus) consistently improve the performance for all methods on all entity types in both collections. Other features produce various results. Our results also show that when trained on limited corpora, Brown clusters resulted in better performance than word embedding clusters did. Further analysis suggests that the entity recognition performance can be greatly boosted through improving the accuracy of entity boundary identification.

show abstract

Detection and categorization of bacteria habitats using shallow linguistic analysis

Cited by 8 publications

References 26 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

Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations

Identifying bacterial biotope entities using sequence labeling: Performance and feature analysis

Contact Info

Product

Resources

About