239 research outputs found
Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry
Chemistry text mining tools should be interoperable and adaptable regardless of
system-level implementation, installation or even programming issues. We aim to
abstract the functionality of these tools from the underlying implementation via
reconfigurable workflows for automatically identifying chemical names. To
achieve this, we refactored an established named entity recogniser (in the
chemistry domain), OSCAR and studied the impact of each component on the net
performance. We developed two reconfigurable workflows from OSCAR using an
interoperable text mining framework, U-Compare. These workflows can be altered
using the drag-&-drop mechanism of the graphical user
interface of U-Compare. These workflows also provide a platform to study the
relationship between text mining components such as tokenisation and named
entity recognition (using maximum entropy Markov model (MEMM) and pattern
recognition based classifiers). Results indicate that, for chemistry in
particular, eliminating noise generated by tokenisation techniques lead to a
slightly better performance than others, in terms of named entity recognition
(NER) accuracy. Poor tokenisation translates into poorer input to the classifier
components which in turn leads to an increase in Type I or Type II errors, thus,
lowering the overall performance. On the Sciborg corpus, the workflow based
system, which uses a new tokeniser whilst retaining the same MEMM component,
increases the F-score from 82.35% to 84.44%. On the PubMed corpus,
it recorded an F-score of 84.84% as against 84.23% by OSCAR
Biomedical Named Entity Recognition: A Review
Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of feature in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features
TechMiner: Extracting Technologies from Academic Publications
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision
Chemical named entities recognition: a review on approaches and applications
The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracte
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