Enhancing information systems management with natural language processing techniques

Métais, Elisabeth

doi:10.1016/s0169-023x(02)00043-5

Cited by 35 publications

(17 citation statements)

References 23 publications

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“…Thus, a preliminary check is needed to screen out invalid web demographics, including records with a company name (i.e., xx.Inc) or a "PO Box" address. In addition to the simple database functions to check null values, natural language processing (NLP) techniques in linguistic science can improve syntax parsing and semantics validation of essential demographic attributes (Métais 2002 ) .…”

Section: Processingmentioning

confidence: 99%

“We Know Who You Are and We Know Where You Live”: A Research Agenda for Web Demographics

Chow

2012

Crowdsourcing Geographic Knowledge

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In the digital era, personal data, such as full name, address, age, phone number, and household members, may scatter across various administrative records, databases of private companies, and social networks, as well as through information contributed as volunteered geographic information (VGI). People-fi nder sites gather such data and provide user interfaces for Internet users to query web demographics. The emergence of web technologies such as mash-ups, web mapping, and webscraping presents opportunities to capitalize on the availability of web demographics and opens up new frontiers of research. The main objectives of this chapter are to (1) examine web demographics as an example of VGI and (2) explore the research agenda of web demographics. More research and development are needed to enhance extraction rules, identify and remove erroneous enumeration (e.g., duplicate, fi ctitious, and incomplete records), validate the coverage and accuracy of web demographics, and explore potential applications. Web demographics must be used cautiously in light of the uncertainty of web demographics (e.g., digital divide), privacy issues, and other societal impacts. This title is partially adapted from Goss ( 1995 ) as a tribute to his early vision of "data merchants" acquiring massive personal demographic data and the instrumentation prospects and potential for resistance to geodemographic marketing systems.

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Section: Processingmentioning

confidence: 99%

“We Know Who You Are and We Know Where You Live”: A Research Agenda for Web Demographics

Chow

2012

Crowdsourcing Geographic Knowledge

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“…This is essential to allow the processing of this kind of information. An interesting review about this technique and utilization in Information System Management is proposed by [12]. In addition, this technology includes the utilization of a series of techniques of normalization which are applied in the extracted concepts: recognition of the punctuation errors to avoid errors which include the inadequate use of punctuation symbols (the tilde, the period, the comma, the point and comma, the dividing bar, etc.…”

Section: A Extraction Of Conceptsmentioning

confidence: 99%

Increasing the efficiency in Non-Technical Losses detection in utility companies

Guerrero

León

Biscarri

et al. 2010

Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference

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Abstract-Usually, the fraud detection method in utility companies uses the consumption information, the economic activity, the geographic location, the active/reactive ration and the contracted power. This paper proposes a combined text mining and neural networks to increase the efficiency in NonTechnical Losses (NTLs) detection methods which was previously applied. This proposed framework proposes to collect all the information that normally cannot be treated with traditional methods. This framework is part of a research project. This project is done in collaboration with Endesa, one of the most important power distribution companies of Europe. Currently, the proposed framework is in the test stage and it uses real cases.

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“…This movement leads to an explosion of activities like information retrieval, natural language understanding, etc. [1][2] [3]. Information retrieval is an art and science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational standalone databases or hypertext networked databases such as the Internet, for text, sound, images or data [4].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Text Question Answering Techniques

Gupta¹,

Gupta²

2012

IJCA

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Question Answering (QA) is a specific type of information retrieval. Given a set of documents, a Question Answering system attempts to find out the correct answer to the question pose in natural language. Question answering is multidisciplinary. It involves information technology, artificial intelligence, natural language processing, knowledge and database management and cognitive science. From the technological perspective, question answering uses natural or statistical language processing, information retrieval, and knowledge representation and reasoning as potential building blocks. It involves text classification, information extraction and summarization technologies. In general, question answering system (QAS) has three components such as question classification, information retrieval, and answer extraction. These components play a essential role in QAS. Question classification play primary role in QA system to categorize the question based upon on the type of its entity. Information retrieval method is get of identify success by extracting out applicable answer post by their intelligent question answering system. Finally, answer extraction module is rising topics in the QAS where these systems are often requiring ranking and validating a candidate's answer.Most of the Question Answering systems consists of three main modules: question processing, document processing and answer processing. Question processing module plays an important part in QA systems. If this module doesn't work correctly, it will make problems for other sections. Moreover answer processing module is an emerging topic in Question Answering, in which these systems are often required to rank and validate candidate answers. These techniques aiming at discovering the short and precise answers are often based on the semantic classification. QA systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.

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Enhancing information systems management with natural language processing techniques

Cited by 35 publications

References 23 publications

“We Know Who You Are and We Know Where You Live”: A Research Agenda for Web Demographics

“We Know Who You Are and We Know Where You Live”: A Research Agenda for Web Demographics

Increasing the efficiency in Non-Technical Losses detection in utility companies

A Survey of Text Question Answering Techniques

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