High Dimensional Hyperbolic Geometry of Complex Networks

High dimensional embeddings of graph data into hyperbolic space have recently been shown to have great value in encoding hierarchical structures, especially in the area of natural language processing, named entity recognition, and machine generation of ontologies. Given the striking success of these approaches, we extend the famous hyperbolic geometric random graph models of Krioukov et al. to arbitrary dimension, providing a detailed analysis of the degree distribution behavior of the model in an expanded portion of the parameter space, considering several regimes which have yet to be considered. Our analysis includes a study of the asymptotic correlations of degree in the network, revealing a non-trivial dependence on the dimension and power law exponent. These results pave the way to using hyperbolic geometric random graph models in high dimensional contexts, which may provide a new window into the internal states of network nodes, manifested only by their external interconnectivity.

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An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning Processing

Data ◽

10.3390/data6070071 ◽

2021 ◽

Vol 6 (7) ◽

pp. 71

Author(s):

Gonçalo Carnaz ◽

Mário Antunes ◽

Vitor Beires Nogueira

Keyword(s):

Machine Learning ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Automatic Identification ◽

Named Entities ◽

Related Data ◽

Named Entity ◽

Chain Of Custody ◽

Evidence Collection

Criminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of 0.808, recall of 0.722, and F1-score of 0.733 were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain.

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Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202286 ◽

2021 ◽

pp. 1-12

Author(s):

Yingwen Fu ◽

Nankai Lin ◽

Xiaotian Lin ◽

Shengyi Jiang

Keyword(s):

Language Processing ◽

State Of The Art ◽

Named Entity Recognition ◽

Entity Recognition ◽

Language Models ◽

Neural Models ◽

Performance Models ◽

Named Entity ◽

High Resource ◽

Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

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Classification of Complex Networks Using Structural Analysis of Random Graph Models

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302344 ◽

2020 ◽

Keyword(s):

Structural Analysis ◽

Complex Networks ◽

Random Graph ◽

Graph Models ◽

Random Graph Models

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

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00008 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 2

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.

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Exponential random graph models for little networks

Social Networks ◽

10.1016/j.socnet.2020.07.005 ◽

2021 ◽

Vol 64 ◽

pp. 225-238

Author(s):

George G. Vega Yon ◽

Andrew Slaughter ◽

Kayla de la Haye

Keyword(s):

Random Graph ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph

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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽

10.3390/info11020079 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Xiaoyu Han ◽

Yue Zhang ◽

Wenkai Zhang ◽

Tinglei Huang

Keyword(s):

Language Processing ◽

Large Scale ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Additional Information ◽

Named Entity ◽

Proposed Model ◽

The Relationship ◽

Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

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A Survey of Arabic Named Entity Recognition and Classification

Computational Linguistics ◽

10.1162/coli_a_00178 ◽

2014 ◽

Vol 40 (2) ◽

pp. 469-510 ◽

Cited By ~ 62

Author(s):

Khaled Shaalan

Keyword(s):

Language Processing ◽

Named Entity Recognition ◽

Relevant Information ◽

Arabic Language ◽

Entity Recognition ◽

Named Entities ◽

Linguistic Resources ◽

Named Entity ◽

To Receive ◽

Made In

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

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

Health Data Science ◽

10.34133/2021/1504854 ◽

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.

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