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Are nearby places (e.g., cities) described by related words? In this article, we transfer this research question in the field of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar, regardless of their distances and the underlying communities of authors. To investigate this, we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way, we come to the conclusion that geographical places, whether close to each other or not, are located in neighboring semantic places that span similar subnetworks in the topic universe.
The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.
CRFVoter : gene and protein related object recognition using a conglomerate of CRF-based tools
(2019)
Background: Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of gene and protein related objects. For this purpose, we transform the task as posed by BioCreative V.5 into a sequence labeling problem. We present a series of sequence labeling systems that we used and adapted in our experiments for solving this task. Our experiments show how to optimize the hyperparameters of the classifiers involved. To this end, we utilize various algorithms for hyperparameter optimization. Finally, we present CRFVoter, a two-stage application of Conditional Random Field (CRF) that integrates the optimized sequence labelers from our study into one ensemble classifier.
Results: We analyze the impact of hyperparameter optimization regarding named entity recognition in biomedical research and show that this optimization results in a performance increase of up to 60%. In our evaluation, our ensemble classifier based on multiple sequence labelers, called CRFVoter, outperforms each individual extractor’s performance. For the blinded test set provided by the BioCreative organizers, CRFVoter achieves an F-score of 75%, a recall of 71% and a precision of 80%. For the GPRO type 1 evaluation, CRFVoter achieves an F-Score of 73%, a recall of 70% and achieved the best precision (77%) among all task participants.
Conclusion: CRFVoter is effective when multiple sequence labeling systems are to be used and performs better then the individual systems collected by it.
LSTMVoter : chemical named entity recognition using a conglomerate of sequence labeling tools
(2019)
Background: Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier.
Results: We introduce LSTMVoter, a bidirectional long short-term memory (LSTM) tagger that utilizes a conditional random field layer in conjunction with attention-based feature modeling. Our approach explores information about features that is modeled by means of an attention mechanism. LSTMVoter outperforms each extractor integrated by it in a series of experiments. On the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus, LSTMVoter achieves an F1-score of 90.04%; on the BioCreative V.5 chemical entity mention in patents corpus, it achieves an F1-score of 89.01%.
Availability and implementation: Data and code are available at https://github.com/texttechnologylab/LSTMVoter.
BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.
With the ongoing loss of global biodiversity, long-term recordings of species distribution patterns are increasingly becoming important to investigate the causes and consequences for their change. Therefore, the digitization of scientific literature, both modern and historical, has been attracting growing attention in recent years. To meet this growing demand the Specialised Information Service for Biodiversity Research (BIOfid) was launched in 2017 with the aim of increasing the availability and accessibility of biodiversity information. Closely tied to the research community the interdisciplinary BIOfid team is digitizing data sources of biodiversity related research and provides a modern and professional infrastructure for hosting and sharing them. As a pilot project, German publications on the distribution and ecology of vascular plants, birds, moths and butterflies covering the past 250 years are prioritized. Large parts of the text corpus defined in accordance with the needs of the relevant German research community have already been transferred to a machine-readable format and will be publicly accessible soon. Software tools for text mining, semantic annotation and analysis with respect to the current trends in machine learning are developed to maximize bioscientific data output through user-specific queries that can be created via the BIOfid web portal (https://www.biofid.de/). To boost knowledge discovery, specific ontologies focusing on morphological traits and taxonomy are being prepared and will continuously be extended to keep up with an ever-expanding volume of literature sources.
In order to promote the accessibility of biodiversity data in historic and contemporary literature, we introduce a new interdisciplinary project called BIOfid (FID=Fachinformationsdienst, a service for providing specialized information). The project aims at a mobilization of data available in print only by combining digitization of scientific biodiversity literature with the development of innovative text mining tools for complex, eventually semantic searches throughout the complete text corpus. A major prerequisite for the development of such search tools is the provision of sophisticated anatomy ontologies on the one hand, and of complete lists of species names (currently considered valid as well as all synonyms) at a global scale on the other hand. In the initial stage, we chose examples from German publications of the past 250 years dealing with the geographic distribution and ecology of vascular plants (Tracheophyta), birds (Aves), as well as moths and butterflies (Lepidoptera) in Germany. These taxa have been prioritized according to current demands of German research groups (about 50 sites) aiming at analyses and modeling of distribution patterns and their changes through time. In the long term, we aim at providing data and open source software applicable for any taxon and geographic region. For this purpose, a platform for open access journals for long-term availability of professional e-journals will be established. All generated data will also be made accessible through GFBio (German Federation for Biological Data). BIOfid is supported by the LIS-Scientific Library Services and Information Systems program of the German Research Foundation (DFG).
The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
In an ideal world, extraction of machine-readable data and knowledge from natural-language biodiversity literature would be done automatically, but not so currently. The BIOfid project has developed some tools that can help with important parts of this highly demanding task, while certain parts of the workflow cannot be automated yet. BIOfid focuses on the 20th century legacy literature, a large part of which is only available in printed form. In this workshop, we will present the current state of the art in mobilisation of data from our corpus, as well as some challenges ahead of us. Together with the participants, we will exercise or explain the following tasks (some of which can be performed by the participants themselves, while other tasks currently require execution by our specialists with special equipment): Preparation of text files as an input; pre-processing with TextImager/TextAnnotator; semiautomated annotation and linking of named entities; generation of output in various formats; evaluation of the output. The workshop will also provide an outlook for further developments regarding extraction of statements from natural-language literature, with the long-term aim to produce machine-readable data from literature that can extend biodiversity databases and knowledge graphs.