The Market for Heritage: Evidence From eBay Using Natural Language Processing

2019 ◽  
pp. 089443931987101 ◽  
Author(s):  
Mark Altaweel
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
The United States ◽  
Entity Recognition ◽  
Cultural Objects ◽  
Online Sales ◽  
The United Kingdom

The trade in antiquities and cultural objects has proven difficult to understand and yet is highly dynamic. Currently, there are few computational tools that allow researchers to systematically understand the nature of the legal market, which can also potentially provide insights into the illegal market such as types of objects traded and countries trading antiquities. Online sales in antiquities and cultural objects are often unstructured data; relevant cultural affiliations, types, and materials for objects are important for distinguishing what might sell, but these data are rarely organized in a format that makes the quantification of sales a simple process. Additionally, sale locations and the total value of sales are relevant to understanding the focus and size of the market. These data all provide potentially useful insights into how the market in antiquities and cultural objects is developing. Based on this, this work presents the results of a machine learning approach using natural language processing and dictionary-based searches that investigate relatively low-end but high sales volume objects sold on eBay’s U.S. site, where sales are often international, between October 2018 and May 2019. The use of named entity recognition, using a conditional random field approach, classifies objects based on the cultures in which they come from, what type of objects they are, and what the objects are made of. The results indicate that objects from the United Kingdom, affiliated with the Roman period, mostly constituting jewelry, and made of metals sell the most. Metal and jewelry objects, in fact, sold more than other object types. Other important countries for selling ancient and cultural objects include the United States, Thailand, Germany, and Cyprus. Some countries appear to more greatly sellspecific types of objects, such as Egypt being a leader in selling Islamic, terracotta, stone, and wood artifacts and Germany selling Viking/early Medieval weapons. Overall, the approach and tool used demonstrate that it is possible to monitor the online antiquities and cultural objects market while potentially gaining useful insights into the market. The tool developed is provided as part of this work so that it can be applied for other cases and online sites, where it can be applied in real time or using historical data.

scholarly journals Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Tomasz Oliwa ◽  
Steven B. Maron ◽  
Leah M. Chase ◽  
Samantha Lomnicki ◽  
Daniel V.T. Catenacci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Named Entity ◽  
Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
George Mastorakos ◽  
Aditya Khurana ◽  
Ming Huang ◽  
Sunyang Fu ◽  
Ahmad P. Tafti ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Corpus Analysis ◽  
Entity Recognition ◽  
Message Content ◽  
Named Entity ◽  
Medical Concepts ◽  
Insight Into

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

scholarly journals DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

2020 ◽  
Vol 10 (18) ◽  
pp. 6429
Author(s):  
SungMin Yang ◽  
SoYeop Yoo ◽  
OkRan Jeong
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Model ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Named Entity ◽  
Artificial Intelligence Technology

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.

OP41 Intercultural Medical Decision Support System Using Natural Language Processing (NLP)

2019 ◽  
Vol 35 (S1) ◽  
pp. 10-10
Author(s):  
Merve Gökgöl ◽  
Zeynep Orhan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Similarity Score ◽  
Entity Recognition ◽  
Medical Decision ◽  
Similarity Function ◽  
String Similarity ◽  
Relevant Today

IntroductionThis study aimed to reach patients using different languages while providing an opportunity to enter symptoms in their everyday language text besides medical expressions of symptoms.MethodologyNamed entity recognition (NER) techniques, based on natural language processing (NLP), were applied to develop a language independent predictive model. The research was based on extracting symptoms entered to the system by patient using NER method of NLP. In order to implement the system, python was used while pre-processing the data and string similarity function was used to estimate similarity with disease symptoms. Two sets were used for classification, one including only symptoms, and the other the matching diseases. Four thousand two hundred and eighty different symptoms were processed for the corresponding 880 diseases.ResultsEach user symptom had a similarity score for each symptom in all diseases. Top N results with highest similarities were chosen from this list. The final N results are matched with diseases. According to these results, matched diseases were ordered in terms of the percentage of matched symptoms in the disease's symptoms. Extracted terms were implied as an input of the model and analysed for a matching diagnosis where an accuracy of 83 percent was accomplished when it is tested and compared using Mayo Clinic data for specific foreign languages other than English.ConclusionThis language independent online diagnostic tool is a solution for both personal and clinical use and provides maintainable, updatable and more reliable diagnostics. This tool is particularly relevant today, with global mobility growing at a rate faster than the world`s population. We aim to upgrade the system by adding speech recognition and engaging it with the background (if available, electronic health records) of the patient.

scholarly journals Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Tiberiu-Marian Georgescu
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Model Based ◽  
The Way

This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.

scholarly journals Large Language Models for Latvian Named Entity Recognition

2020 ◽  
Author(s):  
Rinalds Vīksna ◽  
Inguna Skadiņa
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity ◽  
Language Data

Transformer-based language models pre-trained on large corpora have demonstrated good results on multiple natural language processing tasks for widely used languages including named entity recognition (NER). In this paper, we investigate the role of the BERT models in the NER task for Latvian. We introduce the BERT model pre-trained on the Latvian language data. We demonstrate that the Latvian BERT model, pre-trained on large Latvian corpora, achieves better results (81.91 F1-measure on average vs 78.37 on M-BERT for a dataset with nine named entity types, and 79.72 vs 78.83 on another dataset with seven types) than multilingual BERT and outperforms previously developed Latvian NER systems.

scholarly journals Deep learning in clinical natural language processing: a methodical review

2019 ◽  
Vol 27 (3) ◽  
pp. 457-470 ◽  
Author(s):  
Stephen Wu ◽  
Kirk Roberts ◽  
Surabhi Datta ◽  
Jingcheng Du ◽  
Zongcheng Ji ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

Abstract Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Materials and Methods We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. Results DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a “long tail” of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. Discussion Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). Conclusion Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.

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