IJCRR - Vol 10 Issue 14, July
Date of Publication: 18-Jul-2018
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Natural Language Processing and Unsupervised Learning: It's Significance on Biomedical Literature
Author: Kanika Gupta, Ashok Kumar
Category: Life Sciences
Abstract:There is massive information hidden in the biomedical literature in the form of scientific publications, book chapters, conference reports, etc. This information is growing exponentially with the speed exceeding Moore's Law i.e. observations double in every
two years. It is therefore not possible for researchers and practitioners to automatically extract and relate information from different written resources. Also the data present in the written recourses is unstructured i.e. free-text therefore it becomes very arduous and exorbitant to obtain annotated material for its literature. So in order to overcome these problems Natural Language Processing (NLP) and Unsupervised Learning approaches are used. Natural Language Processing approach is the part of text mining which is the discovery by computer of new, previously unknown information by automatically extracting and relating information from different written resources to reveal the otherwise 'hidden' meanings. The Unsupervised Learning approach is the part of machine learning where no annotated training is necessary and it is more about exploring the data to find insights. Both
these approaches can be used to find knowledge from written textual data in the form different interactions like protein-protein, gene-gene, gene-protein, etc. These approaches could also be used to develop classifiers, databases, tools or softwares which in future would automatically extract the knowledgeable information from literature, answering questions arising in the biomedical research and would also help in the development of new hypothesis. So here we discuss 53 softwares, tools and databases developed using Natural Language Processing (NLP) and unsupervised learning approaches, which are involved in plain texts analyzing and processing, categorizes current work in biomedical information and entities extraction.
Keywords: Text Mining, Natural Language Processing (NLP), Unsupervised Learning and Biomedical Literature
DOI URL: http://dx.doi.org/10.31782/IJCRR.2018.10143
Textual data is considered as the building block upon which any research thrives. The extent of details and the rush of data providing information through the advancements in technologies and internet have increased tremendously. The exponential growth in research for biomedical sciences has led to an increase in its publications. The textual data in the published literature is unstructured or free-text. The unstructured data either does not have a pre-characterized information model or is not sorted out in a pre-characterized way. The information is commonly text-heavy, but may contain critical information in the form of dates, numbers, and facts like protein-protein interactions, gene-disease associations, etc. as well. As the data both communicated and hidden up in biomedical writings are developing exponentially and the composed content is unstructured information so it isn't workable for analysts and experts to naturally extricate and relate data from various compositions (1) (2). Therefore manual effort to transform unstructured text into structured is a laborious process and automatic techniques are the solution (3). There are various automatic techniques for solving the above mentioned issue viz. supervised and unsupervised machine learning, text mining, semantic analysis, artificial intelligence etc. In the current work we will discuss the importance of two automatic techniques i.e. unsupervised machine learning and natural language processing and the softwares, tools and databases developed using these techniques, so that these techniques could be implemented on any biomedical corpus. Natural language processing (NLP) is the ability of a computer program to comprehend human language as it is spoken. The progress in Natural Language Processing (NLP) applications is provocative because computers commonly require humans to "speak" to them in a programming language that is accurate, explicit and highly organized, or through a limited number of clearly articulated voice commands. Most of the research being done on Natural Language Processing (NLP) rotates around search i.e. keyword search or searching relationship entities. This Natural Language Processing (NLP) method enables users to query data sets in the form of a question that they might pose to another person. The machine elucidates the critical components of the human language sentence, such as those that might correspond to specific features in a data set, and returns an answer. Natural Language Processing (NLP) can be utilized to interpret free text and make it analyzable (51). The unsupervised machine learning approaches are generally beneficial on the unstructured data i.e. the data where no labels are given to the learning algorithm, leaving it on its own to find structure in its input. This kind of learning can be a goal in itself by finding hidden patterns in data or a means towards an end i.e. feature learning used for the development of textual classifiers. The unsupervised learning problems can be further grouped into clustering which is problem is where you want to find the inherent groupings in the data (52). Many researchers have utilized these approaches for information extraction from biomedical literature, especially for discovery of protein–protein interactions, gene-protein interaction, gene-drug interaction, etc. In this paper we will discuss few softwares, databases or techniques which use Natural Language Processing (NLP) and unsupervised learning approaches for classification and entity recognition from biofilm literature.
Brief Description of Techniques
This section presents a brief discussion on the Natural Language Processing (NLP) and unsupervised techniques and its general method for linguistics analysis to find different interactions (4).
Natural Language Processing (NLP) methods. Knowledgeable discovery from unstructured text utilizes computational linguistics and philosophy, like syntactic parsing or semantic parsing to analyze sentence structures. Methods of this category define grammars to describe sentence structures and utilize parsers to extract syntactic information and internal dependencies within individual sentences. Approaches in this category can be applied to different knowledge domains after being carefully tuned to the specific problems. But, there is still no guarantee that the performance in the field of biomedicine can achieve comparable performance after tuning. Until recently, methods based on computational linguistics still could not generate satisfactory results (5) (6).
Unsupervised Machine learning. Machine learning broaches to the potentiality of a machine to grasp from experience to extract knowledge from data corpora. As opposed to the aforementioned technique which needs laborious effort to define a set of rules or grammars, machine learning techniques are able to extract protein–protein interaction patterns without human intervention. Statistical approaches are based on word occurrences in a large text corpus. Significant features or patterns are detected and used to classify the abstracts or sentences containing protein–protein interactions, gene-protein interaction and characterize the corresponding relations among genes or proteins. They also define a set of rules for possible textual relationships, called patterns, which encode similar structures in expressing relationships. When combined with statistical methods, scoring schemes depending on the occurrences of patterns to describe the confidence of the relationship are normally used. Similar to computational linguistics methods, rule-based approaches can make use of syntactic information to achieve better performance, although it can also work without prior parsing and tagging of the text (7) (8).
The Figure 1 shows the general outlook of information extraction system from any Biomedical Literature. In this the data is collected from various sources like published articles, scientific journals, books and technical reports, etc and the collected data is in unstructured format. Then using automatic techniques like text mining, text units i.e. words, sentences, paragraphs containing relevant information are generated which needs to be analyzed to get knowledgeable data. Then these text units are further processed and analyzed using unsupervised learning and natural language processing which are used for text classification or clustering on certain textual features and entity recognition like gene-protein interaction, protein-protein interaction, gene-disease interaction, gene-drug interaction, etc. This gathered information can be used for the development of databases, classifiers, softwares, tools or pipelines for future use.
The softwares, tools, databases and pipelines which are involved in information extraction in the form of relationship entities in biofilm literature using Natural Language Processing and Unsupervised Learning approaches are shown in Table 1.
The above mentioned softwares, tools, databases and pipelines can be used for information extraction by initially identifying an item or concept in textual resource and then detecting links between the concepts obtained from the text. By linking the concepts together additional context is given to the concepts, which results in valuable knowledge that can be used for downstream analysis like genome and gene expression annotation, drug-target discovery, drug repositioning, protein-protein interactions, construction of ontologies etc (9). These techniques also help researchers in formulating hypothesis of their future studies as they could find new concepts while analyzing text.
The importance of natural language processing and unsupervised learning depends on the fact that it not only extract information hidden in the biomedical textual data but could also be used for the development of new servers, softwares, databases, etc. These approaches could be used on any biomedical literature. If the above mentioned tools meet all the challenges like specific ontologies describing single disease at various levels, individual pathways and genes for particular diseases, appropriate gene-disease interactions, quality of tool to distinguish between false negative results, etc. being faced in analysis of textual data they will continue to be an indispensable asset for researchers in the biomedical domain (9).
The authors acknowledge the support provided by the online servers, tools and softwares for the compilation of this review. Authors also acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Source of Funding: None
Conflict of Interest: None
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