Mining term networks from text collections for crime investigation

Tseng, Yuen-Hsien; Ho, Zih-Ping; Yang, Kai-Sheng; Chen, Chuncheng

doi:10.1016/j.eswa.2012.02.052

Cited by 32 publications

(19 citation statements)

References 24 publications

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“…To incorporate more semantic information into the SPM, we plan to use language resources such as eHownet, WordNet, and BabelNet for Chinese, English, and multilingual synonym expansion, respectively. Our future study would also use tools like word2vec (Mikolov et al, 2013) and concept map miner (Tseng et al, 2010;Tseng et al, 2012) to extract paradigmatically and/or topically similar terms for term expansion (Tseng et al, 2010). In addition to term expansion, utilization of contextual information of the short texts can be enhanced by machine translation (Tang et al, 2012).…”

Section: Demonstrationmentioning

confidence: 99%

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Tseng

Lee

Chien

et al. 2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.

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Section: Demonstrationmentioning

confidence: 99%

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Tseng

Lee

Chien

et al. 2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

Self Cite

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“…The proposed system primarily discovers useful knowledge for criminal study, and then visualize the extracted criminal network for investigation. Another similar research work is conducted in Ref , where relationship among criminals are extracted from the news articles about crimes. For discovering new knowledge from unstructured documents of police records, a comparative study was performed in another work where usability of emergent self‐organizing map (ESOM) and multi‐dimensional scaling (MDS) were exploited as text exploration instruments to assist police investigations.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Big data analytics for security and criminal investigations

Pramanik

Lau

Yue

et al. 2017

WIREs Data Min & Knowl

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Applications of various data analytics technologies to security and criminal investigation during the past three decades have demonstrated the inception, growth, and maturation of criminal analytics. We first identify five cutting‐edge data mining technologies such as link analysis, intelligent agents, text mining, neural networks, and machine learning. Then, we explore their recent applications to the criminal analytics domain, and discuss the challenges arising from these innovative applications. We also extend our study to big data analytics which provides some state‐of‐the‐art technologies to reshape criminal investigations. In this paper, we review the recent literature, and examine the potentials of big data analytics for security intelligence under a criminal analytics framework. We examine some common data sources, analytics methods, and applications related to two important aspects of social network analysis namely, structural analysis and positional analysis that lay the foundation of criminal analytics. Another contribution of this paper is that we also advocate a novel criminal analytics methodology that is underpinned by big data analytics. We discuss the merits and challenges of applying big data analytics to the criminal analytics domain. Finally, we highlight the future research directions of big data analytics enhanced criminal investigations. WIREs Data Mining Knowl Discov 2017, 7:e1208. doi: 10.1002/widm.1208 This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Computer Architectures for Data Mining

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“…In this paper we consider the most representative type of each approach: (i) Exp network and (ii) Mutual k Nearest Neighbors (kNN) network. In an Exp network, the weight of the relation between a document d i and a document d j (w di,dj ) is given by a Gaussian function, i.e., [1,12], (ii) they co-occur in pieces of texts as sentences/windows [25,14] or in the text collection [29,28,13] (also called similarity), or (iii) they present syntactic/semantic relationship [25,26].…”

Section: Related Work Background and Notationsmentioning

confidence: 99%

Term Network Approach for Transductive Classification

Rossi¹,

Rezende²,

Lopes³

2015

Lecture Notes in Computer Science

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Abstract. Transductive classification is a useful way to classify texts when just few labeled examples are available. Transductive classification algorithms rely on term frequency to directly classify texts represented in vector space model or to build networks and perform label propagation. Related terms tend to belong to the same class and this information can be used to assign relevance scores of terms for classes and consequently the labels of documents. In this paper we propose the use of term networks to model term relations and perform transductive classification. In order to do so, we propose (i) different ways to generate term networks, (ii) how to assign initial relevance scores for terms, (iii) how to propagate the relevance scores among terms, and (iv) how to use the relevance scores of terms in order to classify documents. We demonstrate that transductive classification based on term networks can surpass the accuracies obtained by transductive classification considering texts represented in other types of networks or vector space model, or even the accuracies obtained by inductive classification. We also demonstrated that we can decrease the size of term networks through feature selection while keeping classification accuracy and decreasing computational complexity.

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Mining term networks from text collections for crime investigation

Cited by 32 publications

References 24 publications

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Big data analytics for security and criminal investigations

Term Network Approach for Transductive Classification

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