Motivated by a continually increasing demand for applications that depend on machine comprehension of text-based content, researchers, in both academia and industry, have developed innovative solutions for automated information extraction from text. In this article, we focus on a subset of such tools -i.e., semantic taggers -that not only extract and disambiguate entities mentioned in the text, but also identify topics that unambiguously describe the text's main themes. We offer insight into the process of semantic tagging, the capabilities and specificities of today's semantic taggers, and also indicate some of the criteria to be considered when choosing a tagger to use.

a b s t r a c tConsidering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.

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Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously.

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