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

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

AXEL: A framework to deal with ambiguity in three-noun compounds

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 6/12/2010.Cognitive Linguistics has been widely used to deal with the ambiguity generated by words in combination. Although this domain offers many solutions to address this challenge, not all of them can be implemented in a computational environment. The Dynamic Construal of Meaning framework is argued to have this ability because it describes an intrinsic degree of association of meanings, which in turn, can be translated into computational programs. A limitation towards a computational approach, however, has been the lack of syntactic parameters. This research argues that this limitation could be overcome with the aid of the Generative Lexicon Theory (GLT). Specifically, this dissertation formulated possible means to marry the GLT and Cognitive Linguistics in a novel rapprochement between the two. This bond between opposing theories provided the means to design a computational template (the AXEL System) by realising syntax and semantics at software levels. An instance of the AXEL system was created using a Design Research approach. Planned iterations were involved in the development to improve artefact performance. Such iterations boosted performance-improving, which accounted for the degree of association of meanings in three-noun compounds. This dissertation delivered three major contributions on the brink of a so-called turning point in Computational Linguistics (CL). First, the AXEL system was used to disclose hidden lexical patterns on ambiguity. These patterns are difficult, if not impossible, to be identified without automatic techniques. This research claimed that these patterns can assist audiences of linguists to review lexical knowledge on a software-based viewpoint. Following linguistic awareness, the second result advocated for the adoption of improved resources by decreasing electronic space of Sense Enumerative Lexicons (SELs). The AXEL system deployed the generation of “at the moment of use” interpretations, optimising the way the space is needed for lexical storage. Finally, this research introduced a subsystem of metrics to characterise an ambiguous degree of association of three-noun compounds enabling ranking methods. Weighing methods delivered mechanisms of classification of meanings towards Word Sense Disambiguation (WSD). Overall these results attempted to tackle difficulties in understanding studies of Lexical Semantics via software tools

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Information Extraction from Text for Improving Research on Small Molecules and Histone Modifications

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognition of chemical entities and the new biological named entity type histone modifications, which are both important in the field of drug discovery. As the emergence of new research fields as well as the discovery and generation of novel entities goes along with the coinage of new terms, the perpetual adaptation of respective named entity recognition approaches to new domains is an important step for information extraction. Two methodologies have been investigated in this concern: the state-of-the-art machine learning method, Conditional Random Fields (CRF), and an approximate string search method based on dictionaries. Recognition methods that rely on dictionaries are strongly dependent on the availability of entity terminology collections as well as on its quality. In the case of chemical entities the terminology is distributed over more than 7 publicly available data sources. The join of entries and accompanied terminology from selected resources enables the generation of a new dictionary comprising chemical named entities. Combined with the automatic processing of respective terminology – the dictionary curation – the recognition performance reached an F1 measure of 0.54. That is an improvement by 29 % in comparison to the raw dictionary. The highest recall was achieved for the class of TRIVIAL-names with 0.79. The recognition and identification of chemical named entities provides a prerequisite for the extraction of related pharmacological relevant information from literature data. Therefore, lexico-syntactic patterns were defined that support the automated extraction of hypernymic phrases comprising pharmacological function terminology related to chemical compounds. It was shown that 29-50 % of the automatically extracted terms can be proposed for novel functional annotation of chemical entities provided by the reference database DrugBank. Furthermore, they are a basis for building up concept hierarchies and ontologies or for extending existing ones. Successively, the pharmacological function and biological activity concepts obtained from text were included into a novel descriptor for chemical compounds. Its successful application for the prediction of pharmacological function of molecules and the extension of chemical classification schemes, such as the the Anatomical Therapeutic Chemical (ATC), is demonstrated. In contrast to chemical entities, no comprehensive terminology resource has been available for histone modifications. Thus, histone modification concept terminology was primary recognized in text via CRFs with a F1 measure of 0.86. Subsequent, linguistic variants of extracted histone modification terms were mapped to standard representations that were organized into a newly assembled histone modification hierarchy. The mapping was accomplished by a novel developed term mapping approach described in the thesis. The combination of term recognition and term variant resolution builds up a new procedure for the assembly of novel terminology collections. It supports the generation of a term list that is applicable in dictionary-based methods. For the recognition of histone modification in text it could be shown that the named entity recognition method based on dictionaries is superior to the used machine learning approach. In conclusion, the present thesis provides techniques which enable an enhanced utilization of textual data, hence, supporting research in epigenomics and drug discovery

Natural Language Processing for Technology Foresight Summarization and Simplification: the case of patents

Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts.Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts