Named entity recognition and classification using context Hidden Markov Model

Todorović, Branimir; Rančić, Svetozar R.; Marković, Ivana; Mulalić, Edin H.; Ilić, Vladimir

doi:10.1109/neurel.2008.4685557

Cited by 24 publications

(14 citation statements)

References 3 publications

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“…Semantic descriptions of data are automatically generated during preprocessing and are later used for making autocomplete recommendations to the user, to help him easily search the time series descriptions. Then, key segments are identified related to the given search query, where each of the segments can describe date, time, abnormality in measured physical unit (on some sensor with some features), and all/some unstable data overlapping with an extracted instability interval [64]. In this way, we are able to make more effective and user friendly queries.…”

Section: Semantic Driven 5g System Architecture Challengesmentioning

confidence: 99%

Big Data in 5G Distributed Applications

Nejković

Visa

Tošić

et al. 2019

Lecture Notes in Computer Science

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Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today's networks will become reality-from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.

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Section: Semantic Driven 5g System Architecture Challengesmentioning

confidence: 99%

Big Data in 5G Distributed Applications

Nejković

Visa

Tošić

et al. 2019

Lecture Notes in Computer Science

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“…Prior knowledge of grammar of language is required to develop NER tool for that language. These approaches are successfully implemented in [ Considering the successful implementation of HMM in developing Named Entity Recognition for English language [11], Chinese language [13], as well as for Punjab language [22], and successful implementation of rule based approach [4], this study proposes NER system for Punjabi language using Hybrid approach.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Named Entity Recognition

Bajwa¹,

Kaur²

2015

IJCA

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This paper proposes the Named Entity Recognition (NER) system for Punjabi language using a hybrid approach in which rule based approach and machine learning approach i.e. Hidden Markov Model (HMM) is combined. With no Dataset available, the Named Entities (NEs) were manually tagged which led us to the creation of training and testing dataset, under the linguistic supervision. Using hybrid approach, the proposed system is able to recognize Name of person

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“…More precisely, NER can refer to proper name identifiers, like organizations, people, or location names; time identifiers, like dates, time expressions, or durations; and quantities and numerical expressions, like monetary values, percentages, or phone numbers (Mikheev et al 1999). NER is usually used to tag segments of text, combined with surface text pattern learning (Todorovic et al 2008) in question answering and emerging applications in information extraction. A set of common features used in training the NER system is lexical items, stemmed lexical items, shape, character affixes, part-of-speech, gazetteer, predictive tokens, and the bag-of-words method.…”

Section: Text Identificationmentioning

confidence: 99%

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

et al. 2014

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Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Naïve Bayes, support vector machine, k-nearest neighbor, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while ImageCLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both cateCommunicated by V. Loia. gorization tasks, where a standard evaluation technique (i.e., recall, precision, and F-measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task.

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Named entity recognition and classification using context Hidden Markov Model

Cited by 24 publications

References 3 publications

Big Data in 5G Distributed Applications

Big Data in 5G Distributed Applications

Hybrid Approach for Named Entity Recognition

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

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