Results for “Word Embeddings” — Page 2

DOE OSTI2022

Hierarchical Conceptual Model Event and Activity Domains for Forecasting State-Sponsored Civil Nuclear Power Activities

In FY20, the Savannah River National Laboratory and the Sanghani Center for Artificial Intelligence and Data Analytics (Virginia Tech) entered a collaboration funded by the Department of Energy’s Office of Defense Nuclear Nonproliferation Research and Development to develop a machine learning based modeling pipeline to extract proliferation events of interest from open data sources. The prototype modeling pipeline that was developed relies on the use of time dependent word embedding models to identify contextual shifts in key words and phrases that act as indicators of events of interest. The FY20- 21 efforts were focused on a narrow topical domain of forecasting “fissile core fabrication” at the Savannah River Site prior to its official announcement in 2018. In FY22, the research team was funded to continue development of the modeling pipeline by applying it to the problem of forecasting new, and/or significant changes to existing, civil nuclear power reactors around the world. In this effort, the development will focus on proving applicability to a broader topical domain and in data environments that may contain more sparse information, relative to the United States. In this report, the worldwide landscape of civil nuclear reactors is outlined, the timeline of interest is defined, and a hierarchical conceptual model is established to identify the activity domains of interest and the event domains of interest. This preliminary effort will guide the curation of a glossary of key terms for data acquisition, as well as the downstream modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

ROPE: Recoverable Order-Preserving Embedding of Natural Language

We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and the IMDB Movie Review dataset.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

REGULATORY REQUIREMENTS, GUIDANCE, AND REVIEW OF EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing in the nuclear power market as more and more users both within and outside the nuclear sector are employing EDDs. EDDs in other industries or countries may be known by the related terms as smart devices, devices of limited functionality, or intelligent devices. EDDs can sometimes perform a safety role when embedded in components such as temperature transmitters, pressure transmitters, voltage regulators, gas analyzers, boiler controllers, relays, and radiation monitors. The introduction of EDDs can affect safety by creating new hazards, vulnerabilities, failure modes, triggering mechanisms, and other potential safety concerns at both the component and system levels. The U.S. Nuclear Regulatory Commission’s (NRC’s) existing regulations and guidance cover the use of EDDs in safety-related and important to safety systems in nuclear power plants (NPPs). In addition to potential future challenges, the nature of EDDs being implemented in similar or identical manners between different plants and industries opens up the potential of leveraging the reviews of other regulators or utilization of industry certifications.Key words: embedded digital device, firmware

Halverson, D. S.↗

DOE OSTI2021

OPERATING EXPERIENCE AND ITS RELATIONSHIP TO EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing and the added functionality digital brings such as diagnostics, displays, control functions is welcomed in both the nuclear and other industries. As a result, pure analog-based components continue to disappear from the industrial I&C marketplace. Equipment with EDDs, also known by related terms such as smart devices, intelligent devices, and digital devices of limited functionality, can include sensors, breakers, priority logic modules, time delay relays, pumps, valve actuators, motor control centers, and uninterruptible power supplies. Not only is equipment consisting of analog and older digital technology being replaced with commercial grade products containing EDDs, but EDDs can also be retrofitted to work with existing components. EDDs may be installed physically within a component, such as in a relay. There are also external devices that some would not consider an EDD, but that operate in almost all technical ways identically to one internal to a component such as in a valve controller. In fact, the digital portions may be located hundreds of feet from the actual component.Due to the potential of being used in an identical manner and without alteration from their application in other industries, operating experience (OpE) for EDDs may be much more applicable than in the case of other more system level devices. Clear separation and independence of functionality provided with EDDs may also allow greater applicability of existing OpE from industry for changes that can be shown not to affect its safety function. However, OpE also demonstrates issues with different conditions between other industries and nuclear power plants, such as issues with undeclared changes or undeclared digital devices and the new failure modes they may introduce, issues with the reporting of OpE in industry, and a lack of transparency into OpE for EDDs.Understanding and addressing these issues will be key in the future use of EDDs in nuclear power plants, but failure rate data is difficult to obtain for EDDs, and lessons learned from the use of EDDs are difficult to collect and identify.Key words: embedded digital device, smart device, operating experience, failure modes

Muhlheim, Michael↗

DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.

Sayed, Shehrin↗

DOE OSTI2021

Advances in scientific literature mining for interpreting materials characterization

Abstract Using synchrotron light sources, such as the National Synchrotron Light Source II at Brookhaven National Laboratory, scientists in fields as diverse as physics, biology, and materials science, identify the atomic structure, chemical composition, or other important properties of varied specimens. x-ray spectroscopy from light sources is particularly valuable for materials research with vast information available about reference spectra in the scientific literature. However, as the technique is applicable to many science domains, searching for information about select x-ray spectroscopy spectra is impeded by the sheer number of publications. Moreover, useful information about the context of an experiment or figures presented in papers can be buried among the details, which takes time to assess. This work presents a scientific literature mining system that supports data acquisition, information extraction, and user interaction for referencing x-ray spectra identification and spectral interpretation. The goal is to provide efficient access to useful spectral data to researchers who may spend only a few days at a synchrotron light source. With this system, users browse a classification tree for papers arranged according to x-ray spectroscopic methods, chemical elements, and x-ray absorption spectroscopy edges. Relevant figures are extracted with sentences from the paper that explain them, known as ‘figure explanatory text.’ Notably, this system focuses on semantic aspects (logical analysis) to find figure explanatory text using deep contextualized word embeddings techniques and contains an interface to obtain labeled data from domain experts that is used to evaluate and improve the model.

Park, Gilchan (ORCID:0000000201536646)↗

DOE OSTI2018

ESTEEM

ESTEEM is an interactive web-based prototype visualization tool to allow you to explore dynamic word embeddings across regions using siple keyword queries.

Source record↗

DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗

DOE OSTI2017

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.

concept drift↗

DOE OSTI2023

SULI Oral Presentation

Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

SULI Oral Presentation

Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Network

Language in social media is extremely dynamic: new concepts emerge, trend and disappear, while the meaning of existing concepts can fluctuate over time. This work addresses a novel task of measuring and predicting meaning shift – context or semantic representation change from surface level dynamics e.g., word frequencies observed in social media streams. We study the relationship between language dynamics and short-term context change on a corpus of VKontakte posts collected during the Russia-Ukraine crisis in 2014 – 2015. We train a neural network model to predict short-term shifts in meaning from previous meaning as well as word dynamics. We demonstrate that short-term representation shift can be accurately predicted up to several weeks in advance. Moreover, we visualize this short-term context shift for the example words and demonstrate the practical side of our approach to discover and track meaning of newly emerging terms in social media during crises.

distributional semantics, word embeddings, social ↗

NASA NTRS1980

Influence of auditory fatigue on masked speech intelligibility

Intelligibility of PB word lists embedded in simultaneous masking noise was evaluated before and after fatiguing-noise exposure, which was determined by observing the number of words correctly repeated during a shadowing task. Both the speech signal and the masking noise were filtered to a 2825-3185-Hz band. Masking-noise leves were varied from 0- to 90-dB SL. Fatigue was produced by a 1500-3000-Hz octave band of noise at 115 dB (re 20 micron-Pa) presented continuously for 5 min. The results of three experiments indicated that speed intelligibility was reduced when the speech was presented against a background of silence but that the fatiguing-noise exposure had no effect on intelligibility when the speech was made more intense and embedded in masking noise of 40-90-dB SL. These observations are interpreted by considering the recruitment produced by fatigue and masking noise.

Parker, D. E.↗