14 research outputs found

    Information Extraction From Chemical Patents

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    The development of new chemicals or pharmaceuticals is preceded by an indepth analysis of published patents in this field. This information retrieval is a costly and time inefficient step when done by a human reader, yet it is mandatory for potential success of an investment. The goal of the research project UIMA-HPC is to automate and hence speed-up the process of knowledge mining about patents. Multi-threaded analysis engines, developed according to UIMA (Unstructured Information Management Architecture) standards, process texts and images in thousands of documents in parallel. UNICORE (UNiform Interface to COmputing Resources) workflow control structures make it possible to dynamically allocate resources for every given task to gain best cpu-time/realtime ratios in an HPC environment

    Stress Testing BERT Anaphora Resolution Models for Reaction Extraction in Chemical Patents

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    The high volume of published chemical patents and the importance of a timely acquisition of their information gives rise to automating information extraction from chemical patents. Anaphora resolution is an important component of comprehensive information extraction, and is critical for extracting reactions. In chemical patents, there are five anaphoric relations of interest: co-reference, transformed, reaction associated, work up, and contained. Our goal is to investigate how the performance of anaphora resolution models for reaction texts in chemical patents differs in a noise-free and noisy environment and to what extent we can improve the robustness against noise of the model

    Graph-Based Patent Mining for Mechanical Designs

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    © 2024, IEEE. This is an open access accepted manuscript distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Patents represent a rich source of design innovations, prompting the application of different technologies. Machine learning, text and data mining, similarity scoring, and evolving ontology methods are among the various approaches applied in the literature. This study introduces a schema-free graph data modelling of Functional Analysis Diagrams (FAD) extracted from Patents and their associated Auto-CAD models. It aims to represent mechanical design patents semantically. The schema-free graph model allows for a flexible evolving ontology of known geometries, interactions, and functions. This evolution enables comprehensive queries and ensures efficient storage that is compatible with visualisation libraries. The developed PatMine SolidWorks Add-in ©streamlines CAD design comparison with stored patents’ FAD annotations by highlighting shared concepts. It also enables comprehensive full-text and semantic search queries, similarity scoring, and efficient storage compatible with visualisation libraries. The extraction of functional analysis and geometric features empowers the patent database with capabilities for seamless integration with graph analytics and machine learning approaches for future endeavours

    OSCAR4: a flexible architecture for chemical text-mining

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    RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.Peer Reviewe

    Chemical named entities recognition: a review on approaches and applications

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    The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracte

    A study on developing novel methods for relation extraction

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    Relation Extraction (RE) is a task of Natural Language Processing (NLP) to detect and classify the relations between two entities. Relation extraction in the biomedical and scientific literature domain is challenging as text can contain multiple pairs of entities in the same instance. During the course of this research, we developed an RE framework (RelEx), which consists of five main RE paradigms: rule-based, machine learning-based, Convolutional Neural Network (CNN)-based, Bidirectional Encoder Representations from Transformers (BERT)-based, and Graph Convolutional Networks (GCNs)-based approaches. RelEx\u27s rule-based approach uses co-location information of the entities to determine whether a relation exists between a selected entity and the other entities. RelEx\u27s machine learning-based approach consists of traditional feature representations into traditional machine learning algorithms. RelEx\u27s CNN-based approach consists of three CNN architectures: Segment-CNN, single-label Sentence-CNN, and multi-label Sentence-CNN. RelEx\u27s BERT-based approach utilizes BERT\u27s contextualized word embeddings into a feed-forward neural network. Finally, RelEx\u27s GCN-based approach consists of two GCN-based architectures: GCN-Vanilla, GCN-BERT. We evaluated variations of these approaches in two different domains across four distinct relation types. Overall our findings showed that the rule-based approach is applicable for data with fewer instances in the training data. In contrast, the CNN-based, BERT-based, and GCN-based approaches perform better with labeled data with many training instances. These approaches automatically identify patterns in the data efficiently, whereas rule-based approaches require expert knowledge to generate rules. The CNN-based, BERT-based approaches capture the local contextual information within a sentence or document by embedding both semantic and syntactic information in a learned representation. However, their ability to capture the long-range dependency global information in a text is limited. GCN-based approaches capture the global association information by performing convolution operations on neighbor nodes in a graph and incorporating information from neighbors. Combining GCN with BERT integrates the local contextual and global association information of the words and generates better representations for the words

    Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

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    Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

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