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

Creating a Korean Engineering Academic Vocabulary List (KEAVL): Computational Approach

With a growing number of international students in South Korea, the need for developing materials to study Korean for academic purposes is becoming increasingly pressing. According to statistics, engineering colleges in Korea attract the largest number of international students (Korean National Institute for International Education, 2018). However, despite the availability of technical vocabulary lists for some engineering sub-fields, a list of vocabulary common for the majority of the engineering sub-fields has not yet been built. Therefore, this study was aimed at creating a list of Korean academic vocabulary of engineering for non-native Korean speakers that may help future or first-year engineering students and engineers working in Korea. In order to compile this list, a corpus of Korean textbooks and research articles of 12 major engineering sub-fields, named as the Corpus of Korean Engineering Academic Texts (CKEAT), was compiled. Then, in order to analyze the corpus and compile the preliminary list, I designed a Python-based tool called KWordList. The KWordList lemmatizes all words in the corpus while excluding general Korean vocabulary included in the Korean Learner’s List (Jo, 2003). Then, for the remaining words, KWordList calculates the range, frequency, and dispersion (in this study deviation of proportions or DP (Gries, 2008)) and excludes words that do not pass the study’s criteria (range ≥ 6, frequency ≥ 100, DP ≤ 0.5). The final version of the list, called Korean Engineering Academic Vocabulary List or KEAVL, includes 830 lemmas (318 of intermediate level and 512 of advanced level). For each word, the collocations that occur more than 30 times in the corpus are provided. The comparison of the coverage of the Korean Academic Vocabulary List (Shin, 2004) and KEAVL based on the Corpus of Korean Engineering Academic Texts showed that KEAVL covers more lemmas in the corpus. Moreover, only 313 lemmas from the Korean Academic Vocabulary List (Shin, 2004) passed the criteria of the study. Therefore, KEAVL may be more efficient for engineering students’ vocabulary training than the Korean Academic Vocabulary List and may be used for the engineering Korean teaching materials and curriculum development. Moreover, the KWordList program written for the study can be used by other researchers, teachers, and even students and is open access (https://github.com/HelgaKr/KWordList)

Long-Term Memory for Cognitive Architectures: A Hardware Approach Using Resistive Devices

A cognitive agent capable of reliably performing complex tasks over a long time will acquire a large store of knowledge. To interact with changing circumstances, the agent will need to quickly search and retrieve knowledge relevant to its current context. Real time knowledge search and cognitive processing like this is a challenge for conventional computers, which are not optimised for such tasks. This thesis describes a new content-addressable memory, based on resistive devices, that can perform massively parallel knowledge search in the memory array. The fundamental circuit block that supports this capability is a memory cell that closely couples comparison logic with non-volatile storage. By using resistive devices instead of transistors in both the comparison circuit and storage elements, this cell improves area density by over an order of magnitude compared to state of the art CMOS implementations. The resulting memory does not need power to maintain stored information, and is therefore well suited to cognitive agents with large long-term memories. The memory incorporates activation circuits, which bias the knowledge retrieval process according to past memory access patterns. This is achieved by approximating the widely used base-level activation function using resistive devices to store, maintain and compare activation values. By distributing an instance of this circuit to every row in memory, the activation for all memory objects can be updated in parallel. A test using the word sense disambiguation task shows this circuit-based activation model only incurs a small loss in accuracy compared to exact base-level calculations. A variation of spreading activation can also be achieved in-memory. Memory objects are encoded with high-dimensional vectors that create association between correlated representations. By storing these high-dimensional vectors in the new content-addressable memory, activation can be spread to related objects during search operations. The new memory is scalable, power and area efficient, and performs operations in parallel that are infeasible in real-time for a sequential processor with a conventional memory hierarchy.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201