On the Use of Parsing for Named Entity Recognition

[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150)

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Abstract Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.http://deepblue.lib.umich.edu/bitstream/2027.42/134522/1/13742_2016_Article_117.pd

Optimizing text mining methods for improving biomedical natural language processing

The overwhelming amount and the increasing rate of publication in the biomedical domain make it difficult for life sciences researchers to acquire and maintain all information that is necessary for their research. Pubmed (the primary citation database for the biomedical literature) currently contains over 21 million article abstracts and more than one million of them were published in 2020 alone. Even though existing article databases provide capable keyword search services, typical everyday-life queries usually return thousands of relevant articles. For instance, a cancer research scientist may need to acquire a complete list of genes that interact with BRCA1 (breast cancer 1) gene. The PubMed keyword search for BRCA1 returns over 16,500 article abstracts, making manual inspection of the retrieved documents impractical. Missing even one of the interacting gene partners in this scenario may jeopardize successful development of a potential new drug or vaccine. Although manually curated databases of biomolecular interactions exist, they are usually not up-to-date and they require notable human effort to maintain. To summarize, new discoveries are constantly being shared within the community via scientific publishing, but unfortunately the probability of missing vital information for research in life sciences is increasing. In response to this problem, the biomedical natural language processing (BioNLP) community of researchers has emerged and strives to assist life sciences researchers by building modern language processing and text mining tools that can be applied at large-scale and scan the whole publicly available literature and extract, classify, and aggregate the information found within, thus keeping life sciences researchers always up-to-date with the recent relevant discoveries and facilitating their research in numerous fields such as molecular biology, biomedical engineering, bioinformatics, genetics engineering and biochemistry. My research has almost exclusively focused on biomedical relation and event extraction tasks. These foundational information extraction tasks deal with automatic detection of biological processes, interactions and relations described in the biomedical literature. Precisely speaking, biomedical relation and event extraction systems can scan through a vast amount of biomedical texts and automatically detect and extract the semantic relations of biomedical named entities (e.g. genes, proteins, chemical compounds, and diseases). The structured outputs of such systems (i.e., the extracted relations or events) can be stored as relational databases or molecular interaction networks which can easily be queried, filtered, analyzed, visualized and integrated with other structured data sources. Extracting biomolecular interactions has always been the primary interest of BioNLP researcher because having knowledge about such interactions is crucially important in various research areas including precision medicine, drug discovery, drug repurposing, hypothesis generation, construction and curation of signaling pathways, and protein function and structure prediction. State-of-the-art relation and event extraction methods are based on supervised machine learning, requiring manually annotated data for training. Manual annotation for the biomedical domain requires domain expertise and it is time-consuming. Hence, having minimal training data for building information extraction systems is a common case in the biomedical domain. This demands development of methods that can make the most out of available training data and this thesis gathers all my research efforts and contributions in that direction. It is worth mentioning that biomedical natural language processing has undergone a revolution since I started my research in this field almost ten years ago. As a member of the BioNLP community, I have witnessed the emergence, improvement– and in some cases, the disappearance–of many methods, each pushing the performance of the best previous method one step further. I can broadly divide the last ten years into three periods. Once I started my research, feature-based methods that relied on heavy feature engineering were dominant and popular. Then, significant advancements in the hardware technology, as well as several breakthroughs in the algorithms and methods enabled machine learning practitioners to seriously utilize artificial neural networks for real-world applications. In this period, convolutional, recurrent, and attention-based neural network models became dominant and superior. Finally, the introduction of transformer-based language representation models such as BERT and GPT impacted the field and resulted in unprecedented performance improvements on many data sets. When reading this thesis, I demand the reader to take into account the course of history and judge the methods and results based on what could have been done in that particular period of the history

Complex Network Analysis for Scientific Collaboration Prediction and Biological Hypothesis Generation

With the rapid development of digitalized literature, more and more knowledge has been discovered by computational approaches. This thesis addresses the problem of link prediction in co-authorship networks and protein--protein interaction networks derived from the literature. These networks (and most other types of networks) are growing over time and we assume that a machine can learn from past link creations by examining the network status at the time of their creation. Our goal is to create a computationally efficient approach to recommend new links for a node in a network (e.g., new collaborations in co-authorship networks and new interactions in protein--protein interaction networks). We consider edges in a network that satisfies certain criteria as training instances for the machine learning algorithms. We analyze the neighborhood structure of each node and derive the topological features. Furthermore, each node has rich semantic information when linked to the literature and can be used to derive semantic features. Using both types of features, we train machine learning models to predict the probability of connection for the new node pairs. We apply our idea of link prediction to two distinct networks: a co-authorship network and a protein--protein interaction network. We demonstrate that the novel features we derive from both the network topology and literature content help improve link prediction accuracy. We also analyze the factors involved in establishing a new link and recurrent connections

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Literature mining and network analysis in Biology

Η παρούσα διπλωματική παρουσιάζει το OnTheFly2.0, ένα διαδικτυακό εργαλείο που επικεντρώνεται στην εξαγωγή και επακόλουθη ανάλυση βιοϊατρικών όρων από μεμονωμένα αρχεία. Συγκεκριμένα, το OnTheFly2.0 υποστηρίζει πολλούς διαφορετικούς επιτρέποντας τον παράλληλο χειρισμό τους. Μέσω της ενσωμάτωσης της υπηρεσίας EXTRACT υλοποιείται η Αναγνώριση Ονοματικών Οντοτήτων (Named Entity Recognition) για γονίδια/πρωτεΐνες, χημικές ουσίες, οργανισμούς, ιστούς, περιβάλλοντα, ασθένειες, φαινοτύπους και όρους οντολογίας γονιδίων (Gene Ontology terms), καθώς και η δημιουργία αναδυόμενων παραθύρων που παρέχουν πληροφορίες για τον αναγνωρισμένο όρο, συνοδευόμενες από σύνδεσμο για διάφορες βάσεις δεδομένων. Οι αναγνωρισμένες πρωτεΐνες, τα γονίδια και οι χημικές ουσίες μπορούν να επεξεργαστούν περαιτέρω μέσω αναλύσεων εμπλουτισμού για τη λειτουργικότητα και τη βιβλιογραφία ή να συσχετιστούν με ασθένειες και πρωτεϊνικές δομές. Τέλος, είναι δυνατή η απεικόνιση αλληλεπιδράσεων μεταξύ πρωτεϊνών ή μεταξύ πρωτεϊνών και χημικών ουσιών μέσω της δημιουργίας διαδραστικών δικτύων από τις βάσεις STRING και STITCH αντίστοιχα. Το OnTheFly2.0 υποστηρίζει 197 διαφορετικά είδη οργανισμών και είναι διαθέσιμο στον παρακάτω σύνδεσμο: http://onthefly.pavlopouloslab.info.The particular thesis presents OnTheFly2.0, a web-based, versatile tool dedicated to the extraction and subsequent analysis of biomedical terms from individual files. More specifically, OnTheFly2.0 supports different file formats, enabling simultaneous file handling. The integration of the EXTRACT tagging service allows the implementation of Named Entity Recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and Gene Ontology terms, as well as the generation of popup windows which provide concise, context related information about the identified term, accompanied by links to various databases. Once named entities, such as proteins, genes and chemicals are identified, they can be further explored via functional and publication enrichment analysis or be associated with diseases and protein domains reporting from protein family databases. Finally, visualization of protein-protein and protein-chemical associations is possible through the generation of interactive networks from the STRING and STITCH services, respectively. OnTheFly2.0 currently supports 197 species and is available at http://onthefly.pavlopouloslab.info