Improving key concept extraction using word association measurement

Doan, Phuoc Thi Hong; Arch-int, Ngamnij; Arch-int, Somjit

doi:10.1109/iciteed.2015.7408980

Cited by 4 publications

(8 citation statements)

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“…In this paper, we use two types of the precision [18], such as prior precision and posterior precision to apply the evaluation on lexical level for the obtained results. The prior precision is similar to the precision (Prec) as in Equation (12). Meanwhile, posterior precision (Precpost), used for posterior evaluation by experts, is the proportion of the extracted entities from the corpus that also appear in the golden standard ontology (CorrectExtrEnti) or are correctly judged by human experts (CorrectEvalEnti) with the number of entities that extracted from the corpus (TotalExtrEnti) and showed in Equation (14).…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The term extraction is considered as acquiring nouns and noun phrases in the text corpus. The process of extracting terms was conducted as in [12], consisting of the following steps. First, using Stanford POS tagger tool, 3 nouns and noun phrases are extracted by using the linguistic pattern (JJ) * (NN) + or (JJ) * (NN) * (NNS) + (where, "JJ" is an adjective, "NN" and "NNS" means a singular noun and plural noun respectively, "*": zero or more time occurrences, "+": one or more time occurrences).…”

Section: Term Extractionmentioning

confidence: 99%

“…For example, "national park" is a significant multi-word term, meanwhile "first university" is not. We apply the formula that is based on not only the word occurrence but also the word association to compute the cohesion of individual words in a multi-word term as in [12]. This Equation is described as follows:…”

Section: Term Filteringmentioning

confidence: 99%

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A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Doan

Arch-int

2019

IAJIT

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Nowadays, ontologies have been exploited in many current applications due to the abilities in representing knowledge and inferring new knowledge. However, the manual construction of ontologies is tedious and time-consuming. Therefore, the automated ontology construction from text has been investigated. The extraction of taxonomic relations between concepts is a crucial step in constructing domain ontologies. To obtain taxonomic relations from a text corpus, especially when the data is deficient, the approach of using the web as a source of collective knowledge (a.k.a web-based approach) is usually applied. The important challenge of this approach is how to collect relevant knowledge from a large amount of web pages. To overcome this issue, we propose a framework that combines Word Sense Disambiguation (WSD) and web approach to extract taxonomic relations from a domain-text corpus. This framework consists of two main stages: concept extraction and taxonomic-relation extraction. Concepts acquired from the concept-extraction stage are disambiguated through WSD module and passed to stage of extraction taxonomic relations afterward. To evaluate the efficiency of the proposed framework, we conduct experiments on datasets about two domains of tourism and sport. The obtained results show that the proposed method is efficient in corpora which are insufficient or have no training data. Besides, the proposed method outperforms the state of the art method in corpora having high WSD results.

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Section: Evaluation Methodsmentioning

confidence: 99%

Section: Term Extractionmentioning

confidence: 99%

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A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Doan

Arch-int

2019

IAJIT

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“…This leaves open the key association issue that is the centralization of much late research [5]. Other than the affiliation layer, upper layers, for example, the structure and application layers in like way should trade keys safely [6]. Different security-fundamental applications rely on upon key association philosophy to work likewise request an abnormal state of acclimation to inside disillusionment when a middle point is traded off [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Cluster Based Key Management System Using Signcryption Algorithm for Wireless Sensor Networks

Manikandan¹,

Sakthi²

2018

NNW

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Key management system maintains the confident of secret information from unauthorized users and verifying the integrity of exchanged messages and authenticity. But recent advances in electronics and computer technologies create the complexity of key management in wireless sensor networks (WSN). Additionally, the traditional key management systems are not up to the mark due to limited resources like memory, and energy constraints.In this paper, we propose an optimal cluster based key management system (OC-KMS) for WSNs. The proposed system consist of two contributions, in first, we perform the energy efficient clustering using modified animal Diaspora (MAD)optimization algorithm and cluster head (CH) selection using JAYA trust model. In second contribution, we propose the certificate less signcryption algorithm, which generates and distributes the public and private keys for each node in sensor networks. The proposed system resists various network layer attacks without affecting the network performance. The simulation resultdescribes that the proposed system perform very efficient than existing in terms of both performance and security wise.

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“…In the literature, several approaches have been proposed and tested in diverse domains. The diverse domains are mass gathering (Doan et al , 2015; Kang et al , 2014), lonely planet (Kang et al , 2014), privacy policies (Audich et al , 2017), sport event (Jiang and Tan, 2010), medical (Li and Wu, 2006), Chinese (Qiu et al , 2017), security information management (Abulaish et al , 2011) and insurance product development (Gillani and Kő, 2016). However, most of these approaches have problems with low accuracy rates and poor performance, failing to extract a good set of key concepts from a large set of candidates with long phrases.…”

Section: Introductionmentioning

confidence: 99%

Method for automatic key concepts extraction

Sudarsan

Meenachi

Venkatesh

et al. 2018

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Purpose Ontology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain. Design/methodology/approach In this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database. Findings The work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases. Research limitations/implications The present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also. Practical implications The work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods. Originality/value This work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

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Improving key concept extraction using word association measurement

Cited by 4 publications

References 15 publications

A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Optimal Cluster Based Key Management System Using Signcryption Algorithm for Wireless Sensor Networks

Method for automatic key concepts extraction

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