scholarly journals nLORE: A Linguistically Rich Deep-Learning System for Locative-Reference Extraction in Tweets

2021 ◽  
Author(s):  
Nicolás José Fernández-Martínez ◽  
Carlos Periñán-Pascual
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Real Life ◽  
General Purpose ◽  
Learning System ◽  
Geospatial Data ◽  
Linguistic Knowledge ◽  
Fine Grained ◽  
Domain Specific ◽  
Car Accidents

Location-based systems require rich geospatial data in emergency and crisis-related situations (e.g. earthquakes, floods, terrorist attacks, car accidents or pandemics) for the geolocation of not only a given incident but also the affected places and people in need of immediate help, which could potentially save lives and prevent further damage to urban or environmental areas. Given the sparsity of geotagged tweets, geospatial data must be obtained from the locative references mentioned in textual data such as tweets. In this context, we introduce nLORE (neural LOcative Reference Extractor), a deep-learning system that serves to detect locative references in English tweets by making use of the linguistic knowledge provided by LORE. nLORE, which captures fine-grained complex locative references of any type, outperforms not only LORE, but also well-known general-purpose or domain-specific off-the-shelf entity-recognizer systems, both qualitatively and quantitatively. However, LORE shows much better runtime efficiency, which is especially important in emergency-based and crisis-related scenarios that demand quick intervention to send first responders to affected areas and people. This highlights the often undervalued yet very important role of rule-based models in natural language processing for real-life and real-time scenarios.

LORE: a model for the detection of fine-grained locative references in tweets

2021 ◽  
Author(s):  
Nicolás José Fernández-Martínez ◽  
◽  
Carlos Periñán-Pascual ◽  
Keyword(s):  
Emergency Services ◽  
Situational Awareness ◽  
State Of The Art ◽  
Detection System ◽  
Linguistic Knowledge ◽  
Terrorist Attacks ◽  
Fine Grained ◽  
Points Of Interest ◽  
Car Accidents ◽  
Location Detection

Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.

Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension

2021 ◽  
Vol 30 (6) ◽  
pp. 526-534
Author(s):  
Evelina Fedorenko ◽  
Cory Shain
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Fluid Intelligence ◽  
Linguistic Knowledge ◽  
Domain Specific ◽  
Cognitive Operations ◽  
High Level ◽  
General Circuits ◽  
The Mind ◽  
Language Network

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s lexicon and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension and that past findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation of memory and computation in the mind and brain.

Geoscience Language Processing for Exploration

2021 ◽  
Author(s):  
Huseyin Denli ◽  
Hassan A Chughtai ◽  
Brian Hughes ◽  
Robert Gistri ◽  
Peng Xu
Keyword(s):  
Language Processing ◽  
Similarity Search ◽  
Question Answering ◽  
Language Translation ◽  
Automated Analysis ◽  
General Purpose ◽  
Step Change ◽  
Domain Specific ◽  
Specific Meaning ◽  
Processing Solution

Abstract Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.

OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

2021 ◽  
Author(s):  
Adrit Rao ◽  
Harvey A. Fishman
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Deep Learning ◽  
Early Stage ◽  
Real Life ◽  
Learning System ◽  
Optical Coherence Tomography Image ◽  
Image Analysis System ◽  
Optical Coherence ◽  
Oct Image

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.

Senti-BAS: A BERT-based model with sentiment computing for happiness research (Preprint)

2021 ◽  
Author(s):  
Zeyuan Zeng ◽  
Yijia Zhang ◽  
Liang Yang ◽  
Hongfei Lin
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
High Accuracy ◽  
Language Models ◽  
Fine Grained ◽  
Label Information ◽  
Common Criterion ◽  
Text Content ◽  
Sentiment Computing

BACKGROUND Happiness becomes a rising topic that we all care about recently. It can be described in various forms. For the text content, it is an interesting subject that we can do research on happiness by utilizing natural language processing (NLP) methods. OBJECTIVE As an abstract and complicated emotion, there is no common criterion to measure and describe happiness. Therefore, researchers are creating different models to study and measure happiness. METHODS In this paper, we present a deep-learning based model called Senti-BAS (BERT embedded Bi-LSTM with self-Attention mechanism along with the Sentiment computing). RESULTS Given a sentence that describes how a person felt happiness recently, the model can classify the happiness scenario in the sentence with two topics: was it controlled by the author (label ‘agency’), and was it involving other people (label ‘social’). Besides language models, we employ the label information through sentiment computing based on lexicon. CONCLUSIONS The model performs with a high accuracy on both ‘agency’ and ‘social’ labels, and we also make comparisons with several popular embedding models like Elmo, GPT. Depending on our work, we can study the happiness at a more fine-grained level.

scholarly journals Building Trust in Deep Learning System towards Automated Disease Detection

2019 ◽  
Vol 33 ◽  
pp. 9516-9521 ◽  
Author(s):  
Zhan Wei Lim ◽  
Mong Li Lee ◽  
Wynne Hsu ◽  
Tien Yin Wong
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Images ◽  
Real Life ◽  
High Accuracy ◽  
Learning Systems ◽  
Learning System ◽  
Disease Detection ◽  
Building Trust ◽  
Uncertainty Estimates

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.

IEEE standard upper ontology: a progress report

2002 ◽  
Vol 17 (1) ◽  
pp. 65-70 ◽  
Author(s):  
ADAM PEASE ◽  
IAN NILES
Keyword(s):  
Language Processing ◽  
General Purpose ◽  
Formal Ontology ◽  
Domain Specific ◽  
Automated Inference ◽  
Open Standard ◽  
Additional Domain ◽  
Ieee Standard ◽  
E Mail ◽  
Upper Ontology

The IEEE Standard Upper Ontology (IEEE, 2001) is an effort to create a large, general-purpose, formal ontology. The ontology will be an open standard that can be reused for both academic and commercial purposes without fee, and it will be designed to support additional domain-specific ontologies. The effort is targeted for use in automated inference, semantic interoperability between heterogeneous information systems and natural language processing applications. The effort was begun in May 2000 with an e-mail discussion list, and since then there have been over 6000 e-mail messages among 170 subscribers. These subscribers include representatives from government, academia and industry in various countries. The effort was officially approved as an IEEE standards project in December 2000. Recently a successful workshop was held at IJCAI 2001 to discuss progress and proposals for this project (IJCAI, 2001).

Automatic ontology creation using adaptation

2009 ◽  
Vol 24 (1) ◽  
pp. 127-141 ◽  
Author(s):  
Valerie Cross ◽  
Vishal Bathija
Keyword(s):  
Engineering Design ◽  
Language Processing ◽  
Performance Metrics ◽  
General Purpose ◽  
Information Organization ◽  
Domain Specific ◽  
Organization And Management ◽  
Coverage Accuracy ◽  
Ontology Reuse ◽  
Comparable Quality

AbstractOntologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.

scholarly journals Review of Deep Learning Methods in Robotic Grasp Detection

2018 ◽  
Vol 2 (3) ◽  
pp. 57 ◽  
Author(s):  
Shehan Caldera ◽  
Alexander Rassau ◽  
Douglas Chai
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
General Purpose ◽  
Training Data ◽  
Learning Approaches ◽  
Automated Driving ◽  
Learning Methods ◽  
Robotic Vision ◽  
Object A ◽  
Robotic Grasp

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

scholarly journals Deep Learning for the Russian Language

2020 ◽  
pp. 465-481
Author(s):  
Ekaterina Artemova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Linguistic Diversity ◽  
Real Life ◽  
Russian Language ◽  
Human Vision ◽  
The Russian Language

AbstractDeep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence.While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.

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