An official website of the United States government
Abstract Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi‐file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real‐world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.
more »
« less
“FabNER”: information extraction from manufacturing process science domain literature using named entity recognition
The number of published manufacturing science digital articles available from scientifc journals and the broader web have exponentially increased every year since the 1990s. To assimilate all of this knowledge by a novice engineer or an experienced researcher, requires signifcant synthesis of the existing knowledge space contained within published material, to fnd answers to basic and complex queries. Algorithmic approaches through machine learning and specifcally Natural Language Processing (NLP) on a domain specifc area such as manufacturing, is lacking. One of the signifcant challenges to analyzing manufacturing vocabulary is the lack of a named entity recognition model that enables algorithms to classify the manufacturing corpus of words under various manufacturing semantic categories. This work presents a supervised machine learning approach to categorize unstructured text from 500K+manufacturing science related scientifc abstracts and labelling them under various manufacturing topic categories. A neural network model using a bidirectional long-short term memory, plus a conditional random feld (BiLSTM+CRF) is trained to extract information from manufacturing science abstracts. Our classifer achieves an overall accuracy (f1-score) of 88%, which is quite near to the state-of-the-art performance. Two use case examples are presented that demonstrate the value of the developed NER model as a Technical Language Processing (TLP) workfow on manufacturing science documents. The long term goal is to extract valuable knowledge regarding the connections and relationships between key manufacturing concepts/entities available within millions of manufacturing documents into a structured labeled-property graph data structure that allow for programmatic query and retrieval.
more »
« less
- Award ID(s):
- 1937043
- PAR ID:
- 10290810
- Date Published:
- Journal Name:
- Journal of Intelligent Manufacturing
- ISSN:
- 0956-5515
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Kinases are enzymes that mediate phosphate transfer. Extracting information on kinases from biomedical literature is an important task which has direct implications for applications such as drug design. In this work, we develop KinDER, Kinase Document Extractor and Ranker, a biomedical natural language processing tool for extracting functional and disease related information on kinases. This tool combines information retrieval and machine learning techniques to automatically extract information about protein kinases. First, it uses several bio-ontologies to retrieve documents related to kinases and then uses a supervised classification model to rank them according to their relevance. This was developed to participate in the Text-mining services for Human Kinome Curation Track of the BioCreative VI challenge. According to the official BioCreative evaluation results, KinDER provides stateof- the-art performance for extracting functional information on kinases from abstracts.more » « less
-
We report the preliminary work on cleansing and classifying a scholarly big dataset containing 10+ million academic documents released by CiteSeerX. We design novel approaches to match paper entities in CiteSeerX to reference datasets, including DBLP, Web of Science, and Medline, resulting in 4.2M unique matches, whose metadata can be cleansed. We also investigate traditional machine learning and neural network methods to classify abstracts into 6 subject categories. The classification results reveal that the current CiteSeerX dataset is highly multidisciplinary, containing papers well beyond computer and information sciences.more » « less
-
The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions.more » « less
-
null (Ed.)Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10845-021-01807-x
